Pattichis, M.S.,
Cacoullos, T.,
Soliz, P.[Peter],
New models for region of interest reader classification analysis in
chest radiographs,
PR(42), No. 6, June 2009, pp. 1058-1066.
Elsevier DOI
0902
Region of interest classification; Chest radiographs; ROC analysis;
Binary classification; Pneumoconiosis; Modeling biomedical systems;
Logic, set theory, and algebra; Mathematical procedures and computer
techniques
BibRef
Zhao, W.[Wei],
Xu, R.[Rui],
Hirano, Y.S.[Yasu-Shi],
Tachibana, R.[Rie],
Kido, S.[Shoji],
Suganuma, N.[Narufumi],
Classification of Pneumoconiosis on HRCT Images for Computer-Aided
Diagnosis,
IEICE(E96-D), No. 4, April 2013, pp. 836-844.
WWW Link.
1304
BibRef
Wang, Y.[Ying],
Waylen, P.R.[Peter R.],
Mao, L.[Liang],
Modeling Properties of Influenza-Like Illness Peak Events with
Crossing Theory,
IJGI(3), No. 2, 2014, pp. 764-780.
DOI Link
1407
BibRef
Ouyang, X.,
Huo, J.,
Xia, L.,
Shan, F.,
Liu, J.,
Mo, Z.,
Yan, F.,
Ding, Z.,
Yang, Q.,
Song, B.,
Shi, F.,
Yuan, H.,
Wei, Y.,
Cao, X.,
Gao, Y.,
Wu, D.,
Wang, Q.,
Shen, D.,
Dual-Sampling Attention Network for Diagnosis of COVID-19 From
Community Acquired Pneumonia,
MedImg(39), No. 8, August 2020, pp. 2595-2605.
IEEE DOI
2008
Lung, Computed tomography, Diseases, Hospitals, Radiology,
Image segmentation, COVID-19, COVID-19 Diagnosis, Online Attention,
Dual Sampling Strategy
BibRef
Kang, H.,
Xia, L.,
Yan, F.,
Wan, Z.,
Shi, F.,
Yuan, H.,
Jiang, H.,
Wu, D.,
Sui, H.,
Zhang, C.,
Shen, D.,
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured
Latent Multi-View Representation Learning,
MedImg(39), No. 8, August 2020, pp. 2606-2614.
IEEE DOI
2008
Lung, Computed tomography, Feature extraction, Hospitals, Testing,
COVID-19, COVID-19, Pneumonia, Chest computed tomography (CT),
Multi-view representation learning
BibRef
Wang, X.,
Deng, X.,
Fu, Q.,
Zhou, Q.,
Feng, J.,
Ma, H.,
Liu, W.,
Zheng, C.,
A Weakly-Supervised Framework for COVID-19 Classification and Lesion
Localization From Chest CT,
MedImg(39), No. 8, August 2020, pp. 2615-2625.
IEEE DOI
2008
Computed tomography, Lung, Lesions, Machine learning,
Training, Diseases, COVID-19, COVID-19, CT,
DeCoVNet
BibRef
Fan, D.,
Zhou, T.,
Ji, G.,
Zhou, Y.,
Chen, G.,
Fu, H.,
Shen, J.,
Shao, L.,
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT
Images,
MedImg(39), No. 8, August 2020, pp. 2626-2637.
IEEE DOI
2008
Computed tomography, Image segmentation, Lung, Training, Data models,
Diseases, X-rays, COVID-19, COVID-19, CT image, infection segmentation,
semi-supervised learning
BibRef
Zhou, L.,
Li, Z.,
Zhou, J.,
Li, H.,
Chen, Y.,
Huang, Y.,
Xie, D.,
Zhao, L.,
Fan, M.,
Hashmi, S.,
Abdelkareem, F.,
Eiada, R.,
Xiao, X.,
Li, L.,
Qiu, Z.,
Gao, X.,
A Rapid, Accurate and Machine-Agnostic Segmentation and
Quantification Method for CT-Based COVID-19 Diagnosis,
MedImg(39), No. 8, August 2020, pp. 2638-2652.
IEEE DOI
2008
Computed tomography, Solid modeling, Lung,
Image segmentation, COVID-19, COVID-19,
computerized tomography
BibRef
Wang, G.,
Liu, X.,
Li, C.,
Xu, Z.,
Ruan, J.,
Zhu, H.,
Meng, T.,
Li, K.,
Huang, N.,
Zhang, S.,
A Noise-Robust Framework for Automatic Segmentation of COVID-19
Pneumonia Lesions From CT Images,
MedImg(39), No. 8, August 2020, pp. 2653-2663.
IEEE DOI
2008
Noise measurement, Image segmentation, Lesions, Lung, Training,
COVID-19, COVID-19, convolutional neural network, noisy label,
pneumonia
BibRef
Xie, W.,
Jacobs, C.,
Charbonnier, J.,
van Ginneken, B.,
Relational Modeling for Robust and Efficient Pulmonary Lobe
Segmentation in CT Scans,
MedImg(39), No. 8, August 2020, pp. 2664-2675.
IEEE DOI
2008
Computed tomography, Lung, Image segmentation, Diseases, Convolution,
Neural networks, Training, COVID-19, Computed Tomography, COVID-19,
Segmentation
BibRef
Roy, S.,
Menapace, W.,
Oei, S.,
Luijten, B.,
Fini, E.,
Saltori, C.,
Huijben, I.,
Chennakeshava, N.,
Mento, F.,
Sentelli, A.,
Peschiera, E.,
Trevisan, R.,
Maschietto, G.,
Torri, E.,
Inchingolo, R.,
Smargiassi, A.,
Soldati, G.,
Rota, P.,
Passerini, A.,
van Sloun, R.J.G.,
Ricci, E.,
Demi, L.,
Deep Learning for Classification and Localization of COVID-19 Markers
in Point-of-Care Lung Ultrasound,
MedImg(39), No. 8, August 2020, pp. 2676-2687.
IEEE DOI
2008
Image segmentation, Lung, Ultrasonic imaging, Task analysis,
Pathology, Imaging, Diseases, COVID-19, COVID-19, lung ultrasound, deep learning
BibRef
Oh, Y.,
Park, S.,
Ye, J.C.,
Deep Learning COVID-19 Features on CXR Using Limited Training Data
Sets,
MedImg(39), No. 8, August 2020, pp. 2688-2700.
IEEE DOI
2008
Lung, Diseases, Image segmentation, Training, Neural networks,
Sensitivity, Computed tomography, COVID-19, COVID-19, chest X-ray,
saliency map
BibRef
Seghier, M.L.[Mohamed L.],
The COVID-19 pandemic: What can bioengineers, computer scientists and
big data specialists bring to the table,
IJIST(30), No. 3, 2020, pp. 511-512.
DOI Link
2008
BibRef
Han, Z.,
Wei, B.,
Hong, Y.,
Li, T.,
Cong, J.,
Zhu, X.,
Wei, H.,
Zhang, W.,
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple
Instance Learning,
MedImg(39), No. 8, August 2020, pp. 2584-2594.
IEEE DOI
2008
Computed tomography, Diseases, Lung,
Manuals, Medical diagnostic imaging,
machine learning
BibRef
Wang, J.,
Bao, Y.,
Wen, Y.,
Lu, H.,
Luo, H.,
Xiang, Y.,
Li, X.,
Liu, C.,
Qian, D.,
Prior-Attention Residual Learning for More Discriminative COVID-19
Screening in CT Images,
MedImg(39), No. 8, August 2020, pp. 2572-2583.
IEEE DOI
2008
Lung, Diseases, Computed tomography, Lesions, Task analysis,
Image segmentation, Biomedical imaging, COVID-19, COVID-19,
deep attention learning
BibRef
Farhat, H.[Hanan],
Sakr, G.E.[George E.],
Kilany, R.[Rima],
Deep learning applications in pulmonary medical imaging:
Recent updates and insights on COVID-19,
MVA(31), No. 6, August 2020, pp. Article53.
WWW Link.
2008
BibRef
Afshar, P.[Parnian],
Heidarian, S.[Shahin],
Naderkhani, F.[Farnoosh],
Oikonomou, A.[Anastasia],
Plataniotis, K.N.[Konstantinos N.],
Mohammadi, A.[Arash],
COVID-CAPS: A capsule network-based framework for identification of
COVID-19 cases from X-ray images,
PRL(138), 2020, pp. 638-643.
Elsevier DOI
1806
COVID-19 pandemic, X-ray images, Deep learning, Capsule network
BibRef
Wang, Z.[Zheng],
Xiao, Y.[Ying],
Li, Y.[Yong],
Zhang, J.[Jie],
Lu, F.G.[Fang-Gen],
Hou, M.Z.[Mu-Zhou],
Liu, X.W.[Xiao-Wei],
Automatically discriminating and localizing COVID-19 from
community-acquired pneumonia on chest X-rays,
PR(110), 2021, pp. 107613.
Elsevier DOI
2011
COVID-19, Computer-aided detection (CAD),
Community-acquired pneumonia (CAP), Deep learning (DL), Chest X-ray (CXR)
BibRef
Boulant, O.[Oliver],
Fekom, M.[Mathilde],
Pouchol, C.[Camille],
Evgeniou, T.[Theodoros],
Ovchinnikov, A.[Anton],
Porcher, R.[Raphaël],
Vayatis, N.[Nicolas],
SEAIR Framework Accounting for a Personalized Risk Prediction Score:
Application to the Covid-19 Epidemic,
IPOL(10), 2020, pp. 150-166.
DOI Link
2011
BibRef
Polsinelli, M.[Matteo],
Cinque, L.[Luigi],
Placidi, G.[Giuseppe],
A light CNN for detecting COVID-19 from CT scans of the chest,
PRL(140), 2020, pp. 95-100.
Elsevier DOI
2012
Deep Learning, CNN, Pattern Recognition, COVID-19
BibRef
Sebastianelli, A.[Alessandro],
Mauro, F.[Francesco],
di Cosmo, G.[Gianluca],
Passarini, F.[Fabrizio],
Carminati, M.[Marco],
Ullo, S.L.[Silvia Liberata],
AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based
on Artificial Intelligence Algorithms and Multi-Source Data
Processing,
IJGI(10), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Dey, N.[Nilanjan],
Zhang, Y.D.[Yu-Dong],
Rajinikanth, V.,
Pugalenthi, R.,
Raja, N.S.M.[N. Sri Madhava],
Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays,
PRL(143), 2021, pp. 67-74.
Elsevier DOI
2102
Chest X-Ray, Pneumonia, VGG19 Architecture, Deep-Learning,
Ensemble Feature Scheme
BibRef
Akgundogdu, A.[Abdurrahim],
Detection of pneumonia in chest X-ray images by using 2D discrete
wavelet feature extraction with random forest,
IJIST(31), No. 1, 2021, pp. 82-93.
DOI Link
2102
image classification, machine learning, pneumonia,
random forest, wavelet
BibRef
Feng, J.B.[Jun-Bang],
Guo, Y.[Yi],
Wang, S.[Shike],
Shi, F.[Feng],
Wei, Y.[Ying],
He, Y.C.[Yi-Chu],
Zeng, P.[Ping],
Liu, J.[Jun],
Wang, W.J.[Wen-Jing],
Lin, L.P.[Li-Ping],
Yang, Q.N.[Qing-Ning],
Li, C.M.[Chuan-Ming],
Liu, X.H.[Xing-Hua],
Differentiation between COVID-19 and bacterial pneumonia using
radiomics of chest computed tomography and clinical features,
IJIST(31), No. 1, 2021, pp. 47-58.
DOI Link
2102
bacterial pneumonia, COVID-19, CT, LightGBM, radiomics
BibRef
Öztürk, S.[Saban],
Özkaya, U.[Umut],
Barstugan, M.[Mücahid],
Classification of Coronavirus (COVID-19) from X-ray and CT images
using shrunken features,
IJIST(31), No. 1, 2021, pp. 5-15.
DOI Link
2102
classification, coronavirus, COVID-19, feature extraction,
hand-crafted features, sAE
BibRef
Zhou, T.X.[Tong-Xue],
Canu, S.[Stéphane],
Ruan, S.[Su],
Automatic COVID-19 CT segmentation using U-Net integrated spatial and
channel attention mechanism,
IJIST(31), No. 1, 2021, pp. 16-27.
DOI Link
2102
attention mechanism, COVID-19, CT, deep learning,
focal tversky loss, segmentation
BibRef
Selvaraj, D.[Deepika],
Venkatesan, A.[Arunachalam],
Mahesh, V.G.V.[Vijayalakshmi G. V.],
Raj, A.N.J.[Alex Noel Joseph],
An integrated feature frame work for automated segmentation of
COVID-19 infection from lung CT images,
IJIST(31), No. 1, 2021, pp. 28-46.
DOI Link
2102
artificial intelligence, computed tomography image,
deep neural network, feature extraction,
Zernike moment
BibRef
Chen, Y.R.[Yu-Rong],
Zhang, H.[Hui],
Wang, Y.N.[Yao-Nan],
Yang, Y.M.[Yi-Min],
Zhou, X.[Xianen],
Wu, Q.M.J.[Q. M. Jonathan],
MAMA Net: Multi-Scale Attention Memory Autoencoder Network for
Anomaly Detection,
MedImg(40), No. 3, March 2021, pp. 1032-1041.
IEEE DOI
2103
COVID-19, Image reconstruction, Anomaly detection, Memory modules,
Training, Feature extraction, Computed tomography,
memory autoencoder
BibRef
Zhang, J.,
Xie, Y.,
Pang, G.,
Liao, Z.,
Verjans, J.,
Li, W.,
Sun, Z.,
He, J.,
Li, Y.,
Shen, C.,
Xia, Y.,
Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware
Anomaly Detection,
MedImg(40), No. 3, March 2021, pp. 879-890.
IEEE DOI
2103
Diseases, Lung, COVID-19, X-rays, Anomaly detection, Viruses (medical),
Task analysis, Viral pneumonia screening, deep anomaly detection,
chest X-ray
BibRef
Wu, Y.H.,
Gao, S.H.,
Mei, J.,
Xu, J.,
Fan, D.P.,
Zhang, R.G.,
Cheng, M.M.,
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification
and Segmentation,
IP(30), 2021, pp. 3113-3126.
IEEE DOI
2103
COVID-19, Computed tomography, Image segmentation, Sensitivity,
X-rays, Pandemics, Lung, COVID-19, joint diagnosis, CT classification,
COVID-19 dataset
BibRef
Oulefki, A.[Adel],
Agaian, S.[Sos],
Trongtirakul, T.[Thaweesak],
Kassah Laouar, A.[Azzeddine],
Automatic COVID-19 lung infected region segmentation and measurement
using CT-scans images,
PR(114), 2021, pp. 107747.
Elsevier DOI
2103
Corona-virus Ddisease (COVID-19),
Computer-Aaided Ddetection (CAD), COVID-19 lesion, 3D Visualization
BibRef
Li, J.P.[Jin-Peng],
Zhao, G.M.[Gang-Ming],
Tao, Y.L.[Ya-Ling],
Zhai, P.H.[Peng-Hua],
Chen, H.[Hao],
He, H.G.[Hui-Guang],
Cai, T.[Ting],
Multi-task contrastive learning for automatic CT and X-ray diagnosis
of COVID-19,
PR(114), 2021, pp. 107848.
Elsevier DOI
2103
Computed tomography, X-ray, COVID-19, Deep learning,
Multi-task learning, Contrastive learning
BibRef
Shorfuzzaman, M.[Mohammad],
Hossain, M.S.[M. Shamim],
MetaCOVID: A Siamese neural network framework with contrastive loss
for n-shot diagnosis of COVID-19 patients,
PR(113), 2021, pp. 107700.
Elsevier DOI
2103
COVID-19 diagnosis, Multi-shot learning, Contrastive loss,
CXR images, Siamese network
BibRef
Chen, X.C.[Xiao-Cong],
Yao, L.[Lina],
Zhou, T.[Tao],
Dong, J.M.[Jin-Ming],
Zhang, Y.[Yu],
Momentum contrastive learning for few-shot COVID-19 diagnosis from
chest CT images,
PR(113), 2021, pp. 107826.
Elsevier DOI
2103
COVID-19 diagnosis, Few-shot learning, Contrastive learning, Chest CT images
BibRef
He, K.[Kelei],
Zhao, W.[Wei],
Xie, X.Z.[Xing-Zhi],
Ji, W.[Wen],
Liu, M.X.[Ming-Xia],
Tang, Z.Y.[Zhen-Yu],
Shi, Y.H.[Ying-Huan],
Shi, F.[Feng],
Gao, Y.[Yang],
Liu, J.[Jun],
Zhang, J.F.[Jun-Feng],
Shen, D.G.[Ding-Gang],
Synergistic learning of lung lobe segmentation and hierarchical
multi-instance classification for automated severity assessment of
COVID-19 in CT images,
PR(113), 2021, pp. 107828.
Elsevier DOI
2103
COVID-19, CT, Severity assessment, Lung lobe segmentation,
Multi-instance learning
BibRef
Samulowska, M.[Marta],
Chmielewski, S.[Szymon],
Raczko, E.[Edwin],
Lupa, M.[Michal],
Myszkowska, D.[Dorota],
Zagajewski, B.[Bogdan],
Crowdsourcing without Data Bias: Building a Quality Assurance System
for Air Pollution Symptom Mapping,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Upadhyay, K.[Kamini],
Agrawal, M.[Monika],
Deepak, D.[Desh],
Ensemble learning-based COVID-19 detection by feature boosting in chest
X-ray images,
IET-IPR(14), No. 16, 19 December 2020, pp. 4059-4066.
DOI Link
2103
BibRef
Tiwari, S.[Shamik],
Jain, A.[Anurag],
Convolutional capsule network for COVID-19 detection using
radiography images,
IJIST(31), No. 2, 2021, pp. 525-539.
DOI Link
2105
capsule network, convolutional neural network, COVID-19,
decision support system, deep learning, visual geometry group, X-ray
BibRef
Polat, H.[Hasan],
Özerdem, M.S.[Mehmet Siraç],
Ekici, F.[Faysal],
Akpolat, V.[Veysi],
Automatic detection and localization of COVID-19 pneumonia using
axial computed tomography images and deep convolutional neural
networks,
IJIST(31), No. 2, 2021, pp. 509-524.
DOI Link
2105
classification, computer-aided diagnosis,
convolutional neural networks, coronavirus, COVID-19,
radiology
BibRef
Khan, M.A.[Murtaza Ali],
An automated and fast system to identify COVID-19 from X-ray
radiograph of the chest using image processing and machine learning,
IJIST(31), No. 2, 2021, pp. 499-508.
DOI Link
2105
artificial intelligence, chest X-ray radiograph, COVID-19,
feature descriptors, medical image processing
BibRef
Dhaka, V.S.[Vijaypal Singh],
Rani, G.[Geeta],
Oza, M.G.[Meet Ganpatlal],
Sharma, T.[Tarushi],
Misra, A.[Ankit],
A deep learning model for mass screening of COVID-19,
IJIST(31), No. 2, 2021, pp. 483-498.
DOI Link
2105
CNN model, Corona, COVID-19, deep learning, global pandemic, X-ray
BibRef
El-dosuky, M.A.[Mohamed A.],
Soliman, M.[Mona],
Hassanien, A.E.[Aboul Ella],
COVID-19 vs influenza viruses: A cockroach optimized deep neural
network classification approach,
IJIST(31), No. 2, 2021, pp. 472-482.
DOI Link
2105
cockroach swarm optimization, convolutional neural networks,
coronavirus, COVID-19, deep learning, influenza, SARS-CoV-2
BibRef
Dash, T.K.[Tusar Kanti],
Mishra, S.[Soumya],
Panda, G.[Ganapati],
Satapathy, S.C.[Suresh Chandra],
Detection of COVID-19 from speech signal using bio-inspired based
cepstral features,
PR(117), 2021, pp. 107999.
Elsevier DOI
2106
Bio-inspired computing, COVID19, Speech signal
BibRef
Fan, Y.Q.[Yu-Qi],
Liu, J.H.[Jia-Hao],
Yao, R.X.[Rui-Xuan],
Yuan, X.H.[Xiao-Hui],
COVID-19 Detection from X-ray Images using Multi-Kernel-Size
Spatial-Channel Attention Network,
PR(119), 2021, pp. 108055.
Elsevier DOI
2106
Deep learning, Attention, Coronavirus, X-ray images, Multi-scale
BibRef
de Carvalho Brito, V.[Vitória],
dos Santos, P.R.S.[Patrick Ryan Sales],
de Sales Carvalho, N.R.[Nonato Rodrigues],
de Carvalho Filho, A.O.[Antonio Oseas],
COVID-index: A texture-based approach to classifying lung lesions
based on CT images,
PR(119), 2021, pp. 108083.
Elsevier DOI
2106
COVID-19, Computed tomography, 3D texture analysis, Phylogenetic diversity
BibRef
Irmak, E.[Emrah],
COVID-19 disease severity assessment using CNN model,
IET-IPR(15), No. 8, 2021, pp. 1814-1824.
DOI Link
2106
BibRef
Koyuncu, H.[Hasan],
Barstugan, M.[Mücahid],
COVID-19 discrimination framework for X-ray images by considering
radiomics, selective information, feature ranking, and a novel hybrid
classifier,
SP:IC(97), 2021, pp. 116359.
Elsevier DOI
2107
Binary categorization, Chaotic, Coronavirus, Framework design,
Hybrid classifier, Optimization
BibRef
Zhao, S.X.[Shi-Xuan],
Li, Z.D.[Zhi-Dan],
Chen, Y.[Yang],
Zhao, W.[Wei],
Xie, X.Z.[Xing-Zhi],
Liu, J.[Jun],
Zhao, D.[Di],
Li, Y.J.[Yong-Jie],
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification
from CT images,
PR(119), 2021, pp. 108109.
Elsevier DOI
2108
COVID-19, Convolutional neural network, Segmentation,
Lung opacification, Attention mechanism
BibRef
Shaban, W.M.[Warda M.],
Rabie, A.H.[Asmaa H.],
Saleh, A.I.[Ahmed I.],
Abo-Elsoud, M.A.,
Accurate detection of COVID-19 patients based on distance biased
Naďve Bayes (DBNB) classification strategy,
PR(119), 2021, pp. 108110.
Elsevier DOI
2108
COVID-19, Classification, NB, Feature selection, Wrapper,
Optimization, Particle swarm
BibRef
Hu, J.L.[Jin-Long],
Xu, S.H.[Song-Hua],
Ding, X.D.[Xiang-Dong],
A Triplet network framework based automatic assessment of simulation
quality for respiratory droplet propagation,
PR(119), 2021, pp. 108060.
Elsevier DOI
2108
Simulation quality assessment,
Respiratory droplet propagation, Triplet network, Attentive temporal pooling
BibRef
Vieira, P.[Pablo],
Sousa, O.[Orrana],
Magalhăes, D.[Deborah],
Rabęlo, R.[Ricardo],
Silva, R.[Romuere],
Detecting pulmonary diseases using deep features in X-ray images,
PR(119), 2021, pp. 108081.
Elsevier DOI
2108
COVID-19, X-ray, Deep learning, Pre-processing
BibRef
Zhao, C.[Chen],
Xu, Y.[Yan],
He, Z.[Zhuo],
Tang, J.S.[Jin-Shan],
Zhang, Y.J.[Yi-Jun],
Han, J.G.[Jun-Gang],
Shi, Y.X.[Yu-Xin],
Zhou, W.H.[Wei-Hua],
Lung segmentation and automatic detection of COVID-19 using radiomic
features from chest CT images,
PR(119), 2021, pp. 108071.
Elsevier DOI
2108
COVID-19, Chest CT, Pulmonary parenchyma segmentation,
Deep learning, 3D V-Net
BibRef
Cao, W.[Wen],
Dai, H.R.[Hao-Ran],
Zhu, J.W.[Jing-Wen],
Tian, Y.Z.[Yu-Zhen],
Peng, F.L.[Fei-Lin],
Analysis and Evaluation of Non-Pharmaceutical Interventions on
Prevention and Control of COVID-19: A Case Study of Wuhan City,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Cho, Y.[Yongwon],
Hwang, S.H.[Sung Ho],
Oh, Y.W.[Yu-Whan],
Ham, B.J.[Byung-Joo],
Kim, M.J.[Min Ju],
Park, B.J.[Beom Jin],
Deep convolution neural networks to differentiate between COVID-19
and other pulmonary abnormalities on chest radiographs: Evaluation
using internal and external datasets,
IJIST(31), No. 3, 2021, pp. 1087-1104.
DOI Link
2108
chest radiography, computer-aided diagnosis (CAD), COVID-19,
deep learning, lung diseases
BibRef
Johri, S.[Shikhar],
Goyal, M.[Mehendi],
Jain, S.[Sahil],
Baranwal, M.[Manoj],
Kumar, V.[Vinay],
Upadhyay, R.[Rahul],
A novel machine learning-based analytical framework for automatic
detection of COVID-19 using chest X-ray images,
IJIST(31), No. 3, 2021, pp. 1105-1119.
DOI Link
2108
chest X-ray images, coronavirus, machine learning methods, pneumonia
BibRef
Tang, L.[Lu],
Tian, C.[Chuangeng],
Meng, Y.[Yankai],
Xu, K.[Kai],
Longitudinal evaluation for COVID-19 chest CT disease progression
based on Tchebichef moments,
IJIST(31), No. 3, 2021, pp. 1120-1127.
DOI Link
2108
blur, COVID-19 CT image, disease progression,
objective evaluation, Tchebichef moments
BibRef
Zhang, X.[XiaoQing],
Wang, G.Y.[Guang-Yu],
Zhao, S.G.[Shu-Guang],
COVSeg-NET: A deep convolution neural network for COVID-19 lung CT
image segmentation,
IJIST(31), No. 3, 2021, pp. 1071-1086.
DOI Link
2108
convolution neural network, COVID-19, image segmentation, lung CT image
BibRef
Yu, F.[Fuli],
Zhu, Y.[Yu],
Qin, X.X.[Xiang-Xiang],
Xin, Y.[Ying],
Yang, D.W.[Da-Wei],
Xu, T.[Tao],
A multi-class COVID-19 segmentation network with pyramid attention
and edge loss in CT images,
IET-IPR(15), No. 11, 2021, pp. 2604-2613.
DOI Link
2108
BibRef
Mohammadi, A.[Arash],
Wang, Y.X.[Ying-Xu],
Enshaei, N.[Nastaran],
Afshar, P.[Parnian],
Naderkhani, F.[Farnoosh],
Oikonomou, A.[Anastasia],
Rafiee, J.[Javad],
Rodrigues de Oliveira, H.[Helder],
Yanushkevich, S.[Svetlana],
Plataniotis, K.N.[Konstantinos N.],
Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and
Hypersignal Processing: Challenges, opportunities, and applications,
SPMag(38), No. 5, September 2021, pp. 37-66.
IEEE DOI
2109
COVID-19, Deep learning, Pandemics, Signal processing,
Prognostics and health management, Epidemiology, Monitoring
BibRef
Aversano, L.[Lerina],
Bernardi, M.L.[Mario Luca],
Cimitile, M.[Marta],
Pecori, R.[Riccardo],
Deep neural networks ensemble to detect COVID-19 from CT scans,
PR(120), 2021, pp. 108135.
Elsevier DOI
2109
Deep learning, CT Scan images, COVID-19, Coronavirus
BibRef
Mu, N.[Nan],
Wang, H.Y.[Hong-Yu],
Zhang, Y.[Yu],
Jiang, J.F.[Jing-Feng],
Tang, J.S.[Jin-Shan],
Progressive global perception and local polishing network for lung
infection segmentation of COVID-19 CT images,
PR(120), 2021, pp. 108168.
Elsevier DOI
2109
Coronavirus disease 2019 (COVID-19), Global perception,
Local polishing, Feature recursive aggregation, Multiple supervision
BibRef
Wang, X.F.[Xiao-Fei],
Jiang, L.[Lai],
Li, L.[Liu],
Xu, M.[Mai],
Deng, X.[Xin],
Dai, L.S.[Li-Song],
Xu, X.Y.[Xiang-Yang],
Li, T.Y.[Tian-Yi],
Guo, Y.C.[Yi-Chen],
Wang, Z.[Zulin],
Dragotti, P.L.[Pier Luigi],
Joint Learning of 3D Lesion Segmentation and Classification for
Explainable COVID-19 Diagnosis,
MedImg(40), No. 9, September 2021, pp. 2463-2476.
IEEE DOI
2109
Lesions, COVID-19, Computed tomography,
Task analysis, Databases, Biological system modeling,
deep neural networks
BibRef
Zhang, Y.D.[Yu-Dong],
Zhang, Z.[Zheng],
Zhang, X.[Xin],
Wang, S.H.[Shui-Hua],
MIDCAN: A multiple input deep convolutional attention network for
Covid-19 diagnosis based on chest CT and chest X-ray,
PRL(150), 2021, pp. 8-16.
Elsevier DOI
2109
Deep learning, Data harmonization, Multiple input,
Convolutional neural network, Automatic differentiation,
Multimodality
BibRef
Guarrasi, V.[Valerio],
d'Amico, N.C.[Natascha Claudia],
Sicilia, R.[Rosa],
Cordelli, E.[Ermanno],
Soda, P.[Paolo],
Pareto optimization of deep networks for COVID-19 diagnosis from
chest X-rays,
PR(121), 2022, pp. 108242.
Elsevier DOI
2109
COVID-19, X-ray, Deep-learning, Multi-expert systems,
Optimization, Convolutional neural networks
BibRef
Mansour, R.F.[Romany F.],
Escorcia-Gutierrez, J.[José],
Gamarra, M.[Margarita],
Gupta, D.[Deepak],
Castillo, O.[Oscar],
Kumar, S.[Sachin],
Unsupervised Deep Learning based Variational Autoencoder Model for
COVID-19 Diagnosis and Classification,
PRL(151), 2021, pp. 267-274.
Elsevier DOI
2110
COVID-19, Deep learning, Unsupervised learning,
Variational autoencoder, Image classification
BibRef
Chen, J.G.[Jian-Guo],
Li, K.[Kenli],
Zhang, Z.[Zhaolei],
Li, K.Q.[Ke-Qin],
Yu, P.S.[Philip S.],
A Survey on Applications of Artificial Intelligence in Fighting
Against COVID-19,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link
2110
SARS-CoV-2, COVID-19, Artificial intelligence
BibRef
Yao, Q.S.[Qing-Song],
Xiao, L.[Li],
Liu, P.[Peihang],
Zhou, S.K.[S. Kevin],
Label-Free Segmentation of COVID-19 Lesions in Lung CT,
MedImg(40), No. 10, October 2021, pp. 2808-2819.
IEEE DOI
2110
Lesions, COVID-19, Computed tomography, Lung, Image segmentation,
Training, Task analysis, COVID-19, label-free lesion segmentation,
voxel-level anomaly modeling
BibRef
Aviles-Rivero, A.I.[Angelica I.],
Sellars, P.[Philip],
Schönlieb, C.B.[Carola-Bibiane],
Papadakis, N.[Nicolas],
GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for
identifying COVID-19 on chest X-rays,
PR(122), 2022, pp. 108274.
Elsevier DOI
2112
COVID-19, Chest X-ray, Semi-Supervised learning, Deep learning, Explainability
BibRef
Liu, S.[Shuo],
Han, J.[Jing],
Puyal, E.L.[Estela Laporta],
Kontaxis, S.[Spyridon],
Sun, S.X.[Shao-Xiong],
Locatelli, P.[Patrick],
Dineley, J.[Judith],
Pokorny, F.B.[Florian B.],
Costa, G.D.[Gloria Dalla],
Leocani, L.[Letizia],
Guerrero, A.I.[Ana Isabel],
Nos, C.[Carlos],
Zabalza, A.[Ana],
Sřrensen, P.S.[Per Soelberg],
Buron, M.[Mathias],
Magyari, M.[Melinda],
Ranjan, Y.[Yatharth],
Rashid, Z.[Zulqarnain],
Conde, P.[Pauline],
Stewart, C.[Callum],
Folarin, A.A.[Amos A.],
Dobson, R.J.B.[Richard J.B.],
Bailón, R.[Raquel],
Vairavan, S.[Srinivasan],
Cummins, N.[Nicholas],
Narayan, V.A.[Vaibhav A],
Hotopf, M.[Matthew],
Comi, G.[Giancarlo],
Schuller, B.[Björn],
Consortium, R.C.[RADAR-CNS],
Fitbeat: COVID-19 estimation based on wristband heart rate using a
contrastive convolutional auto-encoder,
PR(123), 2022, pp. 108403.
Elsevier DOI
2112
COVID-19, Respiratory tract infection, Anomaly detection,
Contrastive learning, Convolutional auto-encoder
BibRef
Malhotra, A.[Aakarsh],
Mittal, S.[Surbhi],
Majumdar, P.[Puspita],
Chhabra, S.[Saheb],
Thakral, K.[Kartik],
Vatsa, M.[Mayank],
Singh, R.[Richa],
Chaudhury, S.[Santanu],
Pudrod, A.[Ashwin],
Agrawal, A.[Anjali],
Multi-task driven explainable diagnosis of COVID-19 using chest X-ray
images,
PR(122), 2022, pp. 108243.
Elsevier DOI
2112
X-Ray, COVID-19, Detection, Diagnostics, Deep learning,
Explainable artificial intelligence, Multi-task learning
BibRef
Liu, X.M.[Xiao-Ming],
Yuan, Q.[Quan],
Gao, Y.Z.[Yao-Zong],
He, K.[Kelei],
Wang, S.[Shuo],
Tang, X.[Xiao],
Tang, J.S.[Jin-Shan],
Shen, D.G.[Ding-Gang],
Weakly Supervised Segmentation of COVID19 Infection with Scribble
Annotation on CT Images,
PR(122), 2022, pp. 108341.
Elsevier DOI
2112
COVID-19, infection segmentation, weakly supervised learning,
transformation consistency, uncertainty
BibRef
Kumar, A.[Aayush],
Tripathi, A.R.[Ayush R],
Satapathy, S.C.[Suresh Chandra],
Zhang, Y.D.[Yu-Dong],
SARS-Net: COVID-19 detection from chest x-rays by combining graph
convolutional network and convolutional neural network,
PR(122), 2022, pp. 108255.
Elsevier DOI
2112
Convolutional neural network, Graph convolutional network,
COVID-19 detection, Chest X-ray, Deep learning
BibRef
Bhardwaj, P.[Prashant],
Kaur, A.[Amanpreet],
A novel and efficient deep learning approach for COVID-19 detection
using X-ray imaging modality,
IJIST(31), No. 4, 2021, pp. 1775-1791.
DOI Link
2112
deep learning models, Matthews correlation coefficients,
simple averaging, weighted averaging
BibRef
Lahsaini, I.[Ilyas],
El Habib Daho, M.[Mostafa],
Chikh, M.A.[Mohamed Amine],
Deep transfer learning based classification model for covid-19 using
chest CT-scans,
PRL(152), 2021, pp. 122-128.
Elsevier DOI
2112
Xception, Densenet-121, Densenet-201, COVID-19, Imagenet
BibRef
Ardakani, A.A.[Ali Abbasian],
Kwee, R.M.[Robert M.],
Mirza-Aghazadeh-Attari, M.[Mohammad],
Castro, H.M.[Horacio Matías],
Kuzan, T.Y.[Taha Yusuf],
Altintoprak, K.M.[Kübra Murzoglu],
Besutti, G.[Giulia],
Monelli, F.[Filippo],
Faeghi, F.[Fariborz],
Acharya, U.R.[U Rajendra],
Mohammadi, A.[Afshin],
A practical artificial intelligence system to diagnose COVID-19 using
computed tomography: A multinational external validation study,
PRL(152), 2021, pp. 42-49.
Elsevier DOI
2112
Artificial intelligence, Coronavirus infections,
Machine learning, Pneumonia, Tomography, X-ray computed
BibRef
Abdel-Basset, M.[Mohamed],
Hawash, H.[Hossam],
Moustafa, N.[Nour],
Elkomy, O.M.[Osama M.],
Two-Stage Deep Learning Framework for Discrimination between COVID-19
and Community-Acquired Pneumonia from Chest CT scans,
PRL(152), 2021, pp. 311-319.
Elsevier DOI
2112
BibRef
Xu, G.X.[Geng-Xin],
Liu, C.[Chen],
Liu, J.[Jun],
Ding, Z.X.[Zhong-Xiang],
Shi, F.[Feng],
Guo, M.[Man],
Zhao, W.[Wei],
Li, X.M.[Xiao-Ming],
Wei, Y.[Ying],
Gao, Y.Z.[Yao-Zong],
Ren, C.X.[Chuan-Xian],
Shen, D.G.[Ding-Gang],
Cross-Site Severity Assessment of COVID-19 From CT Images via Domain
Adaptation,
MedImg(41), No. 1, January 2022, pp. 88-102.
IEEE DOI
2201
COVID-19, Computed tomography, Feature extraction, Task analysis,
Prototypes, Pulmonary diseases, Hospitals,
chest computed tomography (CT)
BibRef
Huang, Z.W.[Zi-Wang],
Li, L.[Liang],
Zhang, X.[Xiang],
Song, Y.[Ying],
Chen, J.W.[Jian-Wen],
Zhao, H.Y.[Hui-Ying],
Chong, Y.T.[Yu-Tian],
Wu, H.[Hejun],
Yang, Y.D.[Yue-Dong],
Shen, J.[Jun],
Zha, Y.F.[Yun-Fei],
A coarse-refine segmentation network for COVID-19 CT images,
IET-IPR(16), No. 2, 2022, pp. 333-343.
DOI Link
2201
BibRef
Shiri, I.[Isaac],
Arabi, H.[Hossein],
Salimi, Y.[Yazdan],
Sanaat, A.[Amirhossein],
Akhavanallaf, A.[Azadeh],
Hajianfar, G.[Ghasem],
Askari, D.[Dariush],
Moradi, S.[Shakiba],
Mansouri, Z.[Zahra],
Pakbin, M.[Masoumeh],
Sandoughdaran, S.[Saleh],
Abdollahi, H.[Hamid],
Radmard, A.R.[Amir Reza],
Rezaei-Kalantari, K.[Kiara],
Oghli, M.G.[Mostafa Ghelich],
Zaidi, H.[Habib],
COLI-Net: Deep learning-assisted fully automated COVID-19 lung and
infection pneumonia lesion detection and segmentation from chest
computed tomography images,
IJIST(32), No. 1, 2022, pp. 12-25.
DOI Link
2201
COVID-19, deep learning, pneumonia, segmentation, X-ray CT
BibRef
Cengil, E.[Emine],
Çinar, A.[Ahmet],
The effect of deep feature concatenation in the classification
problem: An approach on COVID-19 disease detection,
IJIST(32), No. 1, 2022, pp. 26-40.
DOI Link
2201
classification, convolutional neural network (CNN), COVID-19,
features concatenation, machine learning algorithms
BibRef
Islam, S.R.[Sheikh Rafiul],
Maity, S.P.[Santi P.],
Ray, A.K.[Ajoy Kumar],
Mandal, M.[Mrinal],
Deep learning on compressed sensing measurements in pneumonia
detection,
IJIST(32), No. 1, 2022, pp. 41-54.
DOI Link
2201
autoencoder, CNN, compressed sensing, deep learning, pneumonia detection
BibRef
Ben Atitallah, S.[Safa],
Driss, M.[Maha],
Boulila, W.[Wadii],
Ben Ghézala, H.[Henda],
Randomly initialized convolutional neural network for the recognition
of COVID-19 using X-ray images,
IJIST(32), No. 1, 2022, pp. 55-73.
DOI Link
2201
COVID-19, deep learning, random initialized CNN, recognition
BibRef
Amini, N.[Nasrin],
Shalbaf, A.[Ahmad],
Automatic classification of severity of COVID-19 patients using
texture feature and random forest based on computed tomography images,
IJIST(32), No. 1, 2022, pp. 102-110.
DOI Link
2201
computed tomography, random forest, severity of COVID-19, texture features
BibRef
Bargshady, G.[Ghazal],
Zhou, X.[Xujuan],
Barua, P.D.[Prabal Datta],
Gururajan, R.[Raj],
Li, Y.F.[Yue-Feng],
Acharya, U.R.[U. Rajendra],
Application of CycleGAN and transfer learning techniques for
automated detection of COVID-19 using X-ray images,
PRL(153), 2022, pp. 67-74.
Elsevier DOI
2201
COVID19, Deep Learning, Transfer Learning, CycleGAN,
Radiological image processing
BibRef
Chen, C.[Chao],
Mao, J.H.[Jin-Hong],
Liu, X.Z.[Xin-Zhi],
Tan, Y.[Yi],
Abaido, G.M.[Ghada M.],
Alsayed, H.[Hamdy],
Compressed feature vector-based effective object recognition model in
detection of COVID-19,
PRL(154), 2022, pp. 60-67.
Elsevier DOI
2202
BibRef
Ben Atitallah, S.[Safa],
Driss, M.[Maha],
Boulila, W.[Wadii],
Koubaa, A.[Anis],
Ben Ghézala, H.[Henda],
Fusion of convolutional neural networks based on Dempster-Shafer
theory for automatic pneumonia detection from chest X-ray images,
IJIST(32), No. 2, 2022, pp. 658-672.
DOI Link
2203
convolutional neural networks, deep learning,
Dempster-Shafer theory, evidence-based fusion,
transfer learning
BibRef
Elghamrawy, S.M.[Sally M.],
Hassanien, A.E.[Aboul Ella],
Vasilakos, A.V.[Athanasios V.],
Genetic-based adaptive momentum estimation for predicting mortality
risk factors for COVID-19 patients using deep learning,
IJIST(32), No. 2, 2022, pp. 614-628.
DOI Link
2203
artificial intelligence, classification algorithms,
deep learning, evolutionary computation, genetic algorithms, predictive model
BibRef
Kumar, A.[Arun],
Mahapatra, R.P.[Rajendra Prasad],
Detection and diagnosis of COVID-19 infection in lungs images using
deep learning techniques,
IJIST(32), No. 2, 2022, pp. 462-475.
DOI Link
2203
classification, convolution neural network, COVID-19,
deep neural network, segmentation
BibRef
Kanwal, S.[Summrina],
Khan, F.[Faiza],
Alamri, S.[Sultan],
Dashtipur, K.[Kia],
Gogate, M.[Mandar],
COVID-opt-aiNet: A clinical decision support system for COVID-19
detection,
IJIST(32), No. 2, 2022, pp. 444-461.
DOI Link
2203
bidirectional long-short-term memory,
clinical decision support system, convolution neural network,
support vector machine
BibRef
Kalayci, M.[Mehmet],
Ayyildiz, H.[Hakan],
Tuncer, S.A.[Seda Arslan],
Bozdag, P.G.[Pinar Gundogan],
Karlidag, G.E.[Gulden Eser],
Can laboratory parameters be an alternative to CT and RT-PCR in the
diagnosis of COVID-19? A machine learning approach,
IJIST(32), No. 2, 2022, pp. 435-443.
DOI Link
2203
artificial intelligence, COVID-19, laboratory parameters, machine learning
BibRef
Tiwari, S.[Shamik],
Jain, A.[Anurag],
A lightweight capsule network architecture for detection of COVID-19
from lung CT scans,
IJIST(32), No. 2, 2022, pp. 419-434.
DOI Link
2203
CapsNet, COVID-19, deep learning, DenseNet, lung CT scan,
MobileNet, ResNet, VGG16
BibRef
Frank, O.[Oz],
Schipper, N.[Nir],
Vaturi, M.[Mordehay],
Soldati, G.[Gino],
Smargiassi, A.[Andrea],
Inchingolo, R.[Riccardo],
Torri, E.[Elena],
Perrone, T.[Tiziano],
Mento, F.[Federico],
Demi, L.[Libertario],
Galun, M.[Meirav],
Eldar, Y.C.[Yonina C.],
Bagon, S.[Shai],
Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound
With Applications to COVID-19,
MedImg(41), No. 3, March 2022, pp. 571-581.
IEEE DOI
2203
COVID-19, Task analysis, Lung, Imaging, Ultrasonic imaging, Semantics,
Training, COVID-19, deep learning, image classification,
semantic segmentation
BibRef
Karthik, R.,
Menaka, R.,
Hariharan, M.,
Won, D.[Daehan],
Contour-enhanced attention CNN for CT-based COVID-19 segmentation,
PR(125), 2022, pp. 108538.
Elsevier DOI
2203
COVID-19, Segmentation, Deep learning, Attention, Decoder, CNN
BibRef
Hu, H.G.[Hai-Gen],
Shen, L.Z.[Lei-Zhao],
Guan, Q.[Qiu],
Li, X.X.[Xiao-Xin],
Zhou, Q.W.[Qian-Wei],
Ruan, S.[Su],
Deep co-supervision and attention fusion strategy for automatic
COVID-19 lung infection segmentation on CT images,
PR(124), 2022, pp. 108452.
Elsevier DOI
2203
Semantic segmentation, Multi-scale features,
Attention mechanism, Feature fusion, COVID-19
BibRef
Bao, G.Q.[Guo-Qing],
Chen, H.[Huai],
Liu, T.L.[Tong-Liang],
Gong, G.Z.[Guan-Zhong],
Yin, Y.[Yong],
Wang, L.S.[Li-Sheng],
Wang, X.[Xiuying],
COVID-MTL: Multitask learning with Shift3D and random-weighted loss
for COVID-19 diagnosis and severity assessment,
PR(124), 2022, pp. 108499.
Elsevier DOI
2203
COVID-19, Multitask learning, 3D CNNs, Diagnosis,
Severity assessment, Deep learning, Computer tomography
BibRef
Dentamaro, V.[Vincenzo],
Giglio, P.[Paolo],
Impedovo, D.[Donato],
Moretti, L.[Luigi],
Pirlo, G.[Giuseppe],
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from
cough and breath,
PR(127), 2022, pp. 108656.
Elsevier DOI
2205
Audio classification, Spectrograms, Attention mechanism, Covid,
Pre-screening, Convolutional neural network
BibRef
Rabie, A.H.[Asmaa H.],
Mansour, N.A.[Nehal A.],
Saleh, A.I.[Ahmed I.],
Takieldeen, A.E.[Ali E.],
Expecting individuals' body reaction to Covid-19 based on statistical
Naďve Bayes technique,
PR(128), 2022, pp. 108693.
Elsevier DOI
2205
Covid-19, Prediction, Naďve Bayes, Prudential Expectation
BibRef
Li, F.[Fudong],
Lu, X.Y.[Xing-Yu],
Yuan, J.J.[Jian-Jun],
MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network
for COVID-19 Chest X-Ray Image Classification,
MedImg(41), No. 5, May 2022, pp. 1208-1218.
IEEE DOI
2205
COVID-19, X-ray imaging, Convolution, Feature extraction,
Pulmonary diseases, Routing, Deep learning, COVID-19,
chest X-ray images
BibRef
Zhou, J.Z.[Jin-Zhao],
Zhang, X.M.[Xing-Ming],
Zhu, Z.W.[Zi-Wei],
Lan, X.Y.[Xiang-Yuan],
Fu, L.K.[Lun-Kai],
Wang, H.X.[Hao-Xiang],
Wen, H.C.[Han-Chun],
Cohesive Multi-Modality Feature Learning and Fusion for COVID-19
Patient Severity Prediction,
CirSysVideo(32), No. 5, May 2022, pp. 2535-2549.
IEEE DOI
2205
COVID-19, Medical diagnostic imaging, Computed tomography,
Hospitals, Visualization, Data models, Computational modeling,
convolutional neural network
BibRef
Sudarshan, V.K.[Vidya K.],
Ramachandra, R.A.[Reshma A.],
Tan, N.S.M.[Nicole Si Min],
Ojha, S.[Smit],
Tan, R.S.[Ru San],
VEntNet: Hybrid deep convolutional neural network model for automated
multi-class categorization of chest X-rays,
IJIST(32), No. 3, 2022, pp. 778-797.
DOI Link
2205
chest X-rays, ChexNet, CNN, COVID, deep neural network, entropy,
GoogleNet, pneumonia, TB, VGG
BibRef
Padmapriya, T.[Thiyagarajan],
Kalaiselvi, T.[Thiruvenkatam],
Priyadharshini, V.[Venugopal],
Multimodal covid network: Multimodal bespoke convolutional neural
network architectures for COVID-19 detection from chest X-ray's and
computerized tomography scans,
IJIST(32), No. 3, 2022, pp. 704-716.
DOI Link
2205
artificial intelligence, chest X-rays,
convolutional neural networks, coronavirus disease, COVID-19,
deep neural networks
BibRef
Bodasingi, N.[Nalini],
Balaji, N.[Narayanam],
Jammu, B.R.[Bhaskara Rao],
Automatic diagnosis of pneumonia using backward elimination method
based SVM and its hardware implementation,
IJIST(32), No. 3, 2022, pp. 1000-1014.
DOI Link
2205
backward elimination method, chest X-ray, support vector machine
BibRef
Wang, W.[Wei],
Huang, W.[Wendi],
Wang, X.[Xin],
Zhang, P.[Peng],
Zhang, N.[Nian],
A COVID-19 CXR image recognition method based on MSA-DDCovidNet,
IET-IPR(16), No. 8, 2022, pp. 2101-2113.
DOI Link
2205
BibRef
Gupta, A.K.[Anuj Kumar],
Sharma, M.[Manvinder],
Sharma, A.[Ankit],
Menon, V.[Vikas],
A Study on SARS-CoV-2 (COVID-19) and Machine Learning Based Approach to
Detect COVID-19 Through X-Ray Images,
IJIG(22), No. 3 2022, pp. 2140010.
DOI Link
2206
BibRef
Novakovic, A.[Aleksandar],
Marshall, A.H.[Adele H.],
The CP-ABM approach for modelling COVID-19 infection dynamics and
quantifying the effects of non-pharmaceutical interventions,
PR(130), 2022, pp. 108790.
Elsevier DOI
2206
COVID-19, Non pharmaceutical interventions,
Change point detection, Agent based model, Genetic algorithm
BibRef
Mannepalli, D.P.[Durga Prasad],
Namdeo, V.[Varsha],
An effective detection of COVID-19 using adaptive dual-stage horse
herd bidirectional long short-term memory framework,
IJIST(32), No. 4, 2022, pp. 1049-1067.
DOI Link
2207
classification, deep learning, feature extraction,
feature selection, optimization, preprocessing
BibRef
Jeong, H.[Hyunsu],
Kim, H.[Hyunwook],
Yoon, J.[Jiwon],
Go, K.[Kyungsup],
Gwak, J.[Jeonghwan],
OVASO: Integrated binary CNN models to classify COVID-19, pneumonia
and healthy lung in X-ray images,
IJIST(32), No. 4, 2022, pp. 1035-1048.
DOI Link
2207
class imbalance, classification, convolutional neural networks,
COVID-19, deep learning, medical imaging, multi-class, transfer learning
BibRef
Ter-Sarkisov, A.[Aram],
One Shot Model for COVID-19 Classification and Lesions Segmentation
in Chest CT Scans Using Long Short-Term Memory Network With Attention
Mechanism,
IEEE_Int_Sys(37), No. 3, May 2022, pp. 54-64.
IEEE DOI
2208
COVID-19, Image segmentation, Feature extraction, Lesions,
Computational modeling, Image classification
BibRef
Xu, M.T.[Meng-Ting],
Zhang, T.[Tao],
Zhang, D.Q.[Dao-Qiang],
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical
Pretrained Models Against Adversarial Attack,
MedImg(41), No. 8, August 2022, pp. 2130-2143.
IEEE DOI
2208
Medical diagnostic imaging, COVID-19, Medical diagnosis, Lesions,
Robustness, Training, Task analysis, Medical image,
robust metric
BibRef
Sharma, A.[Ajay],
Mishra, P.K.[Pramod Kumar],
Covid-MANet: Multi-task attention network for explainable diagnosis
and severity assessment of COVID-19 from CXR images,
PR(131), 2022, pp. 108826.
Elsevier DOI
2208
Covid-19, Lung segmentation, Infection segmentation,
Chest X-ray, Deep learning, Transfer learning, Explainable AI
BibRef
Arora, T.[Tanvi],
CNN-based Prediction of COVID-19 using Chest CT Images,
IJIG(22), No. 4, July 2022, pp. 2250039.
DOI Link
2208
BibRef
Fan, C.D.[Chao-Dong],
Zeng, Z.H.[Zhen-Huan],
Xiao, L.[Leyi],
Qu, X.L.[Xi-Long],
GFNet: Automatic segmentation of COVID-19 lung infection regions
using CT images based on boundary features,
PR(132), 2022, pp. 108963.
Elsevier DOI
2209
Image segmentation, COVID-19, Edge-guidance,
Convolutional neural network, CT image
BibRef
Sunitha, G.[Gurram],
Arunachalam, R.[Rajesh],
Abd-Elnaby, M.[Mohammed],
Eid, M.M.A.[Mahmoud M. A.],
Rashed, A.N.Z.[Ahmed Nabih Zaki],
A comparative analysis of deep neural network architectures for the
dynamic diagnosis of COVID-19 based on acoustic cough features,
IJIST(32), No. 5, 2022, pp. 1433-1446.
DOI Link
2209
convolutional neural network, cough, COVID-19, dilated, temporal
BibRef
Aslan, M.[Muzaffer],
CoviDetNet: A new COVID-19 diagnostic system based on deep features
of chest x-ray,
IJIST(32), No. 5, 2022, pp. 1447-1463.
DOI Link
2209
automatic detection, COVID-19,
deep feature extraction with a lightweight CNN, Relief, SVM
BibRef
Kumar, S.[Sachin],
Shastri, S.[Sourabh],
Mahajan, S.[Shilpa],
Singh, K.[Kuljeet],
Gupta, S.[Surbhi],
Rani, R.[Rajneesh],
Mohan, N.[Neeraj],
Mansotra, V.[Vibhakar],
LiteCovidNet: A lightweight deep neural network model for detection
of COVID-19 using X-ray images,
IJIST(32), No. 5, 2022, pp. 1464-1480.
DOI Link
2209
chest X-ray, classification, COVID-19, deep neural network, LiteCovidNet
BibRef
Polat, H.[Hasan],
A modified DeepLabV3+ based semantic segmentation of chest computed
tomography images for COVID-19 lung infections,
IJIST(32), No. 5, 2022, pp. 1481-1495.
DOI Link
2209
computed tomography, COVID-19, deep learning, DeepLabV3 +,
ResNet, segmentation
BibRef
Chi, J.N.[Jian-Ning],
Zhang, S.[Shuang],
Han, X.Y.[Xiao-Ying],
Wang, H.[Huan],
Wu, C.D.[Cheng-Dong],
Yu, X.S.[Xiao-Sheng],
MID-UNet: Multi-input directional UNet for COVID-19 lung infection
segmentation from CT images,
SP:IC(108), 2022, pp. 116835.
Elsevier DOI
2209
COVID-19, Infection segmentation, CT image, Deep learning,
Convolutional neural networks
BibRef
Sanchez, K.[Karen],
Hinojosa, C.[Carlos],
Arguello, H.[Henry],
Kouamé, D.[Denis],
Meyrignac, O.[Olivier],
Basarab, A.[Adrian],
CX-DaGAN: Domain Adaptation for Pneumonia Diagnosis on a Small Chest
X-Ray Dataset,
MedImg(41), No. 11, November 2022, pp. 3278-3288.
IEEE DOI
2211
Pulmonary diseases, X-ray imaging, Medical diagnostic imaging,
Deep learning, Training, X-rays, Lung, Chest X-ray, deep learning,
pneumonia diagnosis
BibRef
Saberian, M.S.[M. Sadegh],
Moriarty, K.P.[Kathleen P.],
Olmstead, A.D.[Andrea D.],
Hallgrimson, C.[Christian],
Jean, F.[François],
Nabi, I.R.[Ivan R.],
Libbrecht, M.W.[Maxwell W.],
Hamarneh, G.[Ghassan],
DEEMD: Drug Efficacy Estimation Against SARS-CoV-2 Based on Cell
Morphology With Deep Multiple Instance Learning,
MedImg(41), No. 11, November 2022, pp. 3128-3145.
IEEE DOI
2211
Coronaviruses, Drugs, Feature extraction, COVID-19, Compounds,
Morphology, Pipelines, Drug repurposing,
SARS-CoV-2
BibRef
Xiao, B.[Bin],
Yang, Z.[Zeyu],
Qiu, X.M.[Xiao-Ming],
Xiao, J.J.[Jing-Jing],
Wang, G.Y.[Guo-Yin],
Zeng, W.B.[Wen-Bing],
Li, W.S.[Wei-Sheng],
Nian, Y.J.[Yong-Jian],
Chen, W.[Wei],
PAM-DenseNet: A Deep Convolutional Neural Network for Computer-Aided
COVID-19 Diagnosis,
Cyber(52), No. 11, November 2022, pp. 12163-12174.
IEEE DOI
2211
Computed tomography, COVID-19, Lung, Pulmonary diseases,
Feature extraction, Predictive models, Sensitivity,
lung computed tomography (CT) scans
BibRef
Ma, L.[Lu],
Song, S.[Shuni],
Guo, L.T.[Li-Ting],
Tan, W.J.[Wen-Jun],
Xu, L.S.[Li-Sheng],
COVID-19 lung infection segmentation from chest CT images based on
CAPA-ResUNet,
IJIST(33), No. 1, 2023, pp. 6-17.
DOI Link
2301
area loss function, computed tomography (CT) image segmentation, COVID-19,
pre-activated residual block
BibRef
Sanghvi, H.A.[Harshal A.],
Patel, R.H.[Riki H.],
Agarwal, A.[Ankur],
Gupta, S.[Shailesh],
Sawhney, V.[Vivek],
Pandya, A.S.[Abhijit S.],
A deep learning approach for classification of COVID and pneumonia
using DenseNet-201,
IJIST(33), No. 1, 2023, pp. 18-38.
DOI Link
2301
bio-medical innovation, CNN classification, COVID detection,
deep learning, medical imaging, X-ray imaging
BibRef
Gupta, H.[Harsh],
Bansal, N.[Naman],
Garg, S.[Swati],
Mallik, H.[Hritesh],
Prabha, A.[Anju],
Yadav, J.[Jyoti],
A hybrid convolutional neural network model to detect COVID-19 and
pneumonia using chest X-ray images,
IJIST(33), No. 1, 2023, pp. 39-52.
DOI Link
2301
chest X-rays, CNN, COVID-19, hybrid model, pneumonia,
transfer learning techniques
BibRef
Acharya, U.K.[Upendra Kumar],
Ali, M.T.[Mohammad Taha],
Ahmed, M.K.[Mohd Kaif],
Siddiqui, M.T.[Mohd Tabish],
Gupta, H.[Harsh],
Kumar, S.[Sandeep],
Mishra, A.S.[Ajey Shakti],
Hybrid deep neural network for automatic detection of COVID-19 using
chest x-ray images,
IJIST(33), No. 4, 2023, pp. 1129-1143.
DOI Link
2307
BM3D, CLAHE, Darknet, deep convolutional neural network,
inception V3, ResNet50, transfer learning
BibRef
Eyiokur, F.I.[Fevziye Irem],
Kantarci, A.[Alperen],
Erakin, M.E.[Mustafa Ekrem],
Damer, N.[Naser],
Ofli, F.[Ferda],
Imran, M.[Muhammad],
Kriaj, J.[Janez],
Salah, A.A.[Albert Ali],
Waibel, A.[Alexander],
truc, V.[Vitomir],
Ekenel, H.K.[Hazim Kemal],
A survey on computer vision based human analysis in the COVID-19 era,
IVC(130), 2023, pp. 104610.
Elsevier DOI
2301
COVID-19, Human analysis, Masked faces, Survey
BibRef
Liu, Y.B.[Yan-Bei],
Li, H.[Henan],
Luo, T.[Tao],
Zhang, C.Q.[Chang-Qing],
Xiao, Z.T.[Zhi-Tao],
Wei, Y.[Ying],
Gao, Y.Z.[Yao-Zong],
Shi, F.[Feng],
Shan, F.[Fei],
Shen, D.G.[Ding-Gang],
Structural Attention Graph Neural Network for Diagnosis and
Prediction of COVID-19 Severity,
MedImg(42), No. 2, February 2023, pp. 557-567.
IEEE DOI
2302
COVID-19, Lung, Feature extraction, Multitasking,
Computed tomography, Task analysis, Diseases, COVID-19 severity,
multi-task learning
BibRef
Ahmed, N.[Noor],
Tan, X.[Xin],
Ma, L.Z.[Li-Zhuang],
LW-CovidNet: Automatic covid-19 lung infection detection from chest
X-ray images,
IET-IPR(17), No. 2, 2023, pp. 362-374.
DOI Link
2302
BibRef
da Silveira, T.L.T.[Thiago L.T.],
Pinto, P.G.L.[Paulo G.L.],
Lermen, T.S.[Thiago S.],
Jung, C.R.[Cláudio R.],
Omnidirectional 2.5D representation for COVID-19 diagnosis using
chest CTs,
JVCIR(91), 2023, pp. 103775.
Elsevier DOI
2303
2.5D representation, COVID-19 diagnosis, Ground-glass opacity,
Omnidirectional imaging
BibRef
Zhao, A.[Aite],
Wu, H.M.[Hui-Min],
Chen, M.[Ming],
Wang, N.[Nana],
DCACorrCapsNet: A deep channel-attention correlative capsule network
for COVID-19 detection based on multi-source medical images,
IET-IPR(17), No. 4, 2023, pp. 988-1000.
DOI Link
2303
channel-attention, correlative capsule network,
COVID-19 detection, multi-source medical images
BibRef
Lyu, F.[Fei],
Ye, M.[Mang],
Carlsen, J.F.[Jonathan Frederik],
Erleben, K.[Kenny],
Darkner, S.[Sune],
Yuen, P.C.[Pong C],
Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19
Pneumonia Infection Segmentation,
MedImg(42), No. 3, March 2023, pp. 797-809.
IEEE DOI
2303
Image segmentation, COVID-19, Data models, Pulmonary diseases,
Training, Image synthesis, Semantics, Semi-supervised learning,
COVID-19 CT segmentation
BibRef
Wu, X.Y.[Xing-Yu],
Jiang, B.B.[Bing-Bing],
Zhong, Y.[Yan],
Chen, H.H.[Huan-Huan],
Multi-Target Markov Boundary Discovery: Theory, Algorithm, and
Application,
PAMI(45), No. 4, April 2023, pp. 4964-4980.
IEEE DOI
2303
Feature extraction, COVID-19, Task analysis, Meteorology,
Probability distribution, Wind forecasting, Viruses (medical),
target-specific MB variable
BibRef
Kordnoori, S.[Shirin],
Sabeti, M.[Malihe],
Mostafaei, H.[Hamidreza],
Banihashemi, S.S.A.[Saeed Seyed Agha],
Analysis of lung scan imaging using deep multi-task learning
structure for Covid-19 disease,
IET-IPR(17), No. 5, 2023, pp. 1534-1545.
DOI Link
2304
image classification, image segmentation
BibRef
Kordnoori, S.[Shirin],
Sabeti, M.[Maliheh],
Mostafaei, H.[Hamidreza],
Banihashemi, S.S.A.[Saeed Seyed Agha],
An efficient deep multi-task learning structure for COVID-19 disease,
IET-IPR(17), No. 13, 2023, pp. 3728-3745.
DOI Link
2311
image enhancement, medical image processing
BibRef
Han, Z.Y.[Zhong-Yi],
Gui, X.J.[Xian-Jin],
Sun, H.L.[Hao-Liang],
Yin, Y.L.[Yi-Long],
Li, S.[Shuo],
Towards Accurate and Robust Domain Adaptation Under Multiple Noisy
Environments,
PAMI(45), No. 5, May 2023, pp. 6460-6479.
IEEE DOI
2304
Noise measurement, Task analysis, COVID-19, Training,
Machine learning algorithms, X-ray imaging, Supervised learning,
non-stationary environments
BibRef
Jeevitha, S.,
Valarmathi, K.,
A joint segmentation and classification framework for COVID-19
infection segmentation and detection from chest CT images,
IJIST(33), No. 3, 2023, pp. 789-806.
DOI Link
2305
attention, COVID-19, CT images, densenet, multi-task learning, YNet
BibRef
Patel, R.K.[Rajneesh Kumar],
Kashyap, M.[Manish],
Automated diagnosis of COVID stages using texture-based Gabor
features in variational mode decomposition from CT images,
IJIST(33), No. 3, 2023, pp. 807-821.
DOI Link
2305
COVID-19, CT image, Gabor filter, machine learning, VMD
BibRef
Agrali, M.[Mahmut],
Kilic, V.[Volkan],
Onan, A.[Aytug],
Koç, E.M.[Esra Meltem],
Koç, A.M.[Ali Murat],
Büyüktoka, R.E.[Rasit Eren],
Acar, T.[Türker],
Adibelli, Z.[Zehra],
DeepChestNet: Artificial intelligence approach for COVID-19 detection
on computed tomography images,
IJIST(33), No. 3, 2023, pp. 776-788.
DOI Link
2305
artificial intelligence, computer-aided diagnosis system,
COVID-19 detection, lung segmentation, pulmonary lobe segmentation
BibRef
Zeng, L.L.[Ling-Li],
Gao, K.[Kai],
Hu, D.[Dewen],
Feng, Z.C.[Zhi-Chao],
Hou, C.P.[Chen-Ping],
Rong, P.F.[Peng-Fei],
Wang, W.[Wei],
SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening
and Lesion Segmentation,
PAMI(45), No. 8, August 2023, pp. 10427-10442.
IEEE DOI
2307
COVID-19, Lesions, Image segmentation, Lung, Computed tomography,
Labeling, Training, COVID-19, CT image, deep learning, diagnosis,
semi-supervised learning
BibRef
Zhang, Y.D.[Yu-Dong],
Fighting against COVID-19: Innovations and applications,
IJIST(33), No. 4, 2023, pp. 1111-1115.
DOI Link
2307
BibRef
Ibrahim, A.U.[Abdullahi Umar],
Kibarer, A.G.[Ayse Gunnay],
Al-Turjman, F.[Fadi],
Kaba, S.[Serife],
Large-scaled detection of COVID-19 from X-ray using transfer learning,
IJIST(33), No. 4, 2023, pp. 1116-1128.
DOI Link
2307
AlexNet, COVID-19, CT-scan, deep learning, SARS-CoV-2, SVM, X-ray
BibRef
Erdem, K.[Kenan],
Kobat, M.A.[Mehmet Ali],
Bilen, M.N.[Mehmet Nail],
Balik, Y.[Yunus],
Alkan, S.[Sevim],
Cavlak, F.[Feyzanur],
Poyraz, A.K.[Ahmet Kursad],
Barua, P.D.[Prabal Datta],
Tuncer, I.[Ilknur],
Dogan, S.[Sengul],
Baygin, M.[Mehmet],
Erten, M.[Mehmet],
Tuncer, T.[Turker],
Tan, R.S.[Ru-San],
Acharya, U.R.[U. Rajendra],
Hybrid-Patch-Alex: A new patch division and deep feature
extraction-based image classification model to detect COVID-19, heart
failure, and other lung conditions using medical images,
IJIST(33), No. 4, 2023, pp. 1144-1159.
DOI Link
2307
AlexNet, biomedical image classification,
CT image classification, Hybrid-Patch-Alex, transfer learning
BibRef
Bagwan, F.[Faraz],
Pise, N.[Nitin],
A precise and timely graph-based approach to identify SARS Covid19
infection from medical imaging data using IsoCovNet,
IJIST(33), No. 4, 2023, pp. 1160-1176.
DOI Link
2307
convolutional networks, CT-scan,
graph isomorphism network (GIN), graph neural network (GNN), x-ray
BibRef
Samantaray, L.[Leena],
Panda, R.[Rutuparna],
Naik, M.K.[Manoj Kumar],
Abraham, A.[Ajith],
A novel adaptive class weight adjustment-based multiclass
segmentation error minimization technique for COVID-19 X-ray image
analysis,
IJIST(33), No. 4, 2023, pp. 1177-1193.
DOI Link
2307
biomedical image processing, COVID-19 X-ray image analysis,
multiclass segmentation, radiology
BibRef
Das, D.[Dolly],
Biswas, S.K.[Saroj Kumar],
Bandyopadhyay, S.[Sivaji],
Mixed attention and regularized COVID-19 network:
An approach to detection of COVID-19 with chest x-ray images,
IJIST(33), No. 4, 2023, pp. 1194-1222.
DOI Link
2307
channel feature extraction, COVID-19,
deep convolutional neural network, mixed attention, spatial feature extraction
BibRef
Ewaidat, H.A.[Haytham Al],
Balawi, S.[Sara],
Bataineh, Z.[Ziad],
Al-Dwairi, A.[Ahmed],
Al-Khalily, M.[Majd],
Azez, K.A.[Khalaf Abdel],
Almakhadmeh, A.[Ali],
Establishment of national diagnostic reference levels as guidelines
for computed tomography radiation in Jordan,
IJIST(33), No. 4, 2023, pp. 1223-1234.
DOI Link
2307
CT scan, CTDIv, diagnostic reference levels
BibRef
Yan, N.[Nan],
Tao, Y.[Ye],
Pneumonia X-ray detection with anchor-free detection framework and
data augmentation,
IJIST(33), No. 4, 2023, pp. 1235-1246.
DOI Link
2307
anchor-free detection framework, computer-aided diagnosis,
data augmentation, pneumonia detection
BibRef
Hertel, R.[Robert],
Benlamri, R.[Rachid],
Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based
on Radiological Imaging,
Surveys(55), No. 12, March 2023, pp. xx-yy.
DOI Link
2307
neural networks, radiology, Machine learning, infectious disease
BibRef
Naz, M.[Maria],
Shah, M.A.[Munam Ali],
Khattak, H.A.[Hasan Ali],
Wahid, A.[Abdul],
Asghar, M.N.[Muhammad Nabeel],
Rauf, H.T.[Hafiz Tayyab],
Khan, M.A.[Muhammad Attique],
Ameer, Z.[Zoobia],
Multi-branch sustainable convolutional neural network for disease
classification,
IJIST(33), No. 5, 2023, pp. 1621-1633.
DOI Link
2310
BibRef
And:
Correction:
IJIST(34), No. 5, 2024, pp. e23153.
DOI Link
2408
chest computerized tomography (CT) scan,
disease classification, machine learning, corona virus disease-19 (COVID-19)
BibRef
Sameer, H.A.[Humam Adnan],
Mutlag, A.H.[Ammar Hussein],
Gharghan, S.K.[Sadik Kamel],
Deep learning-based COVID-19 diagnosis using CT scans with laboratory
and physiological parameters,
IET-IPR(17), No. 11, 2023, pp. 3127-3142.
DOI Link
2310
convolutional neural network, computed tomography scan,
COVID-19, deep learning, diagnosis, physiological parameters
BibRef
Lu, F.F.[Fang-Fang],
Zhang, Z.H.[Zhi-Hao],
Liu, T.X.[Tian-Xiang],
Tang, C.[Chi],
Bai, H.[Hualin],
Zhai, G.T.[Guang-Tao],
Chen, J.J.[Jing-Jing],
Wu, X.X.[Xiao-Xin],
A weakly supervised inpainting-based learning method for lung CT
image segmentation,
PR(144), 2023, pp. 109861.
Elsevier DOI
2310
COVID-19, Weakly supervised, Lesion segmentation, Image inpainting
BibRef
Dong, A.[Aimei],
Liu, J.[Jian],
Zhang, G.D.[Guo-Dong],
Wei, Z.[Zhonghe],
Zhai, Y.[Yi],
Lv, G.H.[Guo-Hua],
Momentum contrast transformer for COVID-19 diagnosis with knowledge
distillation,
PR(143), 2023, pp. 109732.
Elsevier DOI
2310
Momentum contrastive learning, Knowledge distillation,
Vision transformer, COVID-19 diagnosis
BibRef
Varma, O.R.[Om Ramakisan],
Kalra, M.[Mala],
Kirmani, S.[Sheeraz],
COVID-19: A systematic review of prediction and classification
techniques,
IJIST(33), No. 6, 2023, pp. 1829-1857.
DOI Link
2311
COVID-19 datasets, COVID-19 detection,
COVID-19 manual diagnosis, deep learning, machine learning
BibRef
Mhamdi, L.[Lotfi],
Dammak, O.[Oussama],
Cottin, F.[François],
Ben Dhaou, I.[Imed],
Deep learning for COVID-19 contamination analysis and prediction
using ECG images on Raspberry Pi 4,
IJIST(33), No. 6, 2023, pp. 1858-1869.
DOI Link
2311
COVID-19, deep learning, ECG images, embedded systems,
healthcare, SARS-CoV-2 virus
BibRef
Yang, Y.[Yuan],
Zhang, L.[Lin],
Ren, L.[Lei],
Wang, X.H.[Xiao-Han],
Distributed autoencoder classifier network for small-scale and
scattered COVID-19 dataset classification,
IJIST(33), No. 6, 2023, pp. 1870-1881.
DOI Link
2311
autoencoder, COVID-19 dataset, deep learning, small-scale
BibRef
Salama, A.N.[Aya Nader],
Mohamed, M.A.,
Amer, H.M.[Hanan M.],
Ata, M.M.[Mohamed Maher],
An efficient quantification of COVID-19 in chest CT images with
improved semantic segmentation using U-Net deep structure,
IJIST(33), No. 6, 2023, pp. 1882-1901.
DOI Link
2311
COVID-19 quantification, deep learning, image processing,
semantic segmentation, U-Net
BibRef
Perumal, M.[Murukessan],
Srinivas, M.,
DenSplitnet: Classifier-invariant neural network method to detect
COVID-19 in chest CT data,
JVCIR(97), 2023, pp. 103949.
Elsevier DOI
2312
Chest-CT-scan images, Novel coronavirus, COVID-19,
Deep learning, Self-supervised learning, Computer vision
BibRef
Madan, S.[Shipra],
Chaudhury, S.[Santanu],
Gandhi, T.K.[Tapan Kumar],
Explainable few-shot learning with visual explanations on a low
resource pneumonia dataset,
PRL(176), 2023, pp. 109-116.
Elsevier DOI
2312
Explainable few-shot learning, Medical image analysis,
Back-propagation, Pneumonia, Chest x-ray
BibRef
Singh, P.[Pritpal],
Huang, Y.P.[Yo-Ping],
An Ambiguous Edge Detection Method for Computed Tomography Scans of
Coronavirus Disease 2019 Cases,
SMCS(54), No. 1, January 2024, pp. 352-364.
IEEE DOI
2312
BibRef
Ali, Z.[Zeeshan],
Khan, M.A.[Muhammad Attique],
Hamza, A.[Ameer],
Alzahrani, A.I.[Ahmed Ibrahim],
Alalwan, N.[Nasser],
Shabaz, M.[Mohammad],
Khan, F.[Faheem],
A deep learning-based x-ray imaging diagnosis system for
classification of tuberculosis, COVID-19, and pneumonia traits using
evolutionary algorithm,
IJIST(34), No. 1, 2024, pp. e23014.
DOI Link
2401
COVID-19, deep learning, feature fusion, feature selection,
pneumonia, tuberculosis
BibRef
Naik, M.K.[Manoj Kumar],
Swain, M.[Monorama],
Panda, R.[Rutuparna],
Abraham, A.[Ajith],
Novel Square Error Minimization-Based Multilevel Thresholding Method
for COVID-19 X-Ray Image Analysis Using Fast Cuckoo Search,
IJIG(24), No. 1, January 2024, pp. 2450004.
DOI Link
2402
BibRef
Ali, M.U.[Muhammad Umair],
Zafar, A.[Amad],
Tanveer, J.[Jawad],
Khan, M.A.[Muhammad Attique],
Kim, S.H.[Seong Han],
Alsulami, M.M.[Mashael M.],
Lee, S.W.[Seung Won],
Deep learning network selection and optimized information fusion for
enhanced COVID-19 detection,
IJIST(34), No. 2, 2024, pp. e23001.
DOI Link
2402
COVID-19, deep learning network, fibrosis, pneumonia, tuberculosis, X-rays
BibRef
Shiri, I.[Isaac],
Salimi, Y.[Yazdan],
Saberi, A.[Abdollah],
Pakbin, M.[Masoumeh],
Hajianfar, G.[Ghasem],
Avval, A.H.[Atlas Haddadi],
Sanaat, A.[Amirhossein],
Akhavanallaf, A.[Azadeh],
Mostafaei, S.[Shayan],
Mansouri, Z.[Zahra],
Askari, D.[Dariush],
Ghasemian, M.[Mohammadreza],
Sharifipour, E.[Ehsan],
Sandoughdaran, S.[Saleh],
Sohrabi, A.[Ahmad],
Sadati, E.[Elham],
Livani, S.[Somayeh],
Iranpour, P.[Pooya],
Kolahi, S.[Shahriar],
Khosravi, B.[Bardia],
Khateri, M.[Maziar],
Bijari, S.[Salar],
Atashzar, M.R.[Mohammad Reza],
Shayesteh, S.P.[Sajad P.],
Babaei, M.R.[Mohammad Reza],
Jenabi, E.[Elnaz],
Hasanian, M.[Mohammad],
Shahhamzeh, A.[Alireza],
Ghomi, S.Y.F.[Seyed Yaser Foroghi],
Mozafari, A.[Abolfazl],
Shirzad-Aski, H.[Hesamaddin],
Movaseghi, F.[Fatemeh],
Bozorgmehr, R.[Rama],
Goharpey, N.[Neda],
Abdollahi, H.[Hamid],
Geramifar, P.[Parham],
Radmard, A.R.[Amir Reza],
Arabi, H.[Hossein],
Rezaei-Kalantari, K.[Kiara],
Oveisi, M.[Mehrdad],
Rahmim, A.[Arman],
Zaidi, H.[Habib],
Differentiation of COVID-19 pneumonia from other lung diseases using
CT radiomic features and machine learning:
A large multicentric cohort study,
IJIST(34), No. 2, 2024, pp. e23028.
DOI Link
2402
computed tomography, COVID-19, differential diagnosis,
machine learning, radiomics
BibRef
Islam, M.K.[Md Khairul],
Rahman, M.M.[Md Mahbubur],
Ali, M.S.[Md Shahin],
Mahim, S.M.,
Miah, M.S.[Md Sipon],
Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU
model with explainable AI: A deep learning approach,
IVC(142), 2024, pp. 104918.
Elsevier DOI
2402
Lung cancer, COVID-19, Pre-processing, Feature extraction,
DCNN-ViT-GRU, Explainable AI
BibRef
Wu, H.[Huisi],
Zhang, B.M.[Bai-Ming],
Chen, C.[Cheng],
Qin, J.[Jing],
Federated Semi-Supervised Medical Image Segmentation via
Prototype-Based Pseudo-Labeling and Contrastive Learning,
MedImg(43), No. 2, February 2024, pp. 649-661.
IEEE DOI Code:
WWW Link.
2402
Data models, Biomedical imaging, Prototypes, Training,
Distributed databases, Servers, COVID-19, Federated learning,
medical imaging segmentation
BibRef
Xie, J.S.[Jun-Song],
Wu, Q.[Qian],
Zhu, R.[Renju],
Entropy-guided contrastive learning for semi-supervised medical image
segmentation,
IET-IPR(18), No. 2, 2024, pp. 312-326.
DOI Link
2402
contrastive learning, Covid-19, entropy-guided,
medical image segmentation, semi-supervised learning
BibRef
Deng, S.Q.[Shi-Qi],
Zhang, X.[Xing],
Jiang, S.C.[Shan-Cheng],
A diagnostic report supervised deep learning model training strategy
for diagnosis of COVID-19,
PR(149), 2024, pp. 110232.
Elsevier DOI
2403
Deep learning, COVID-19, Image classification, Diagnosis,
Chest CT image, Multimodal
BibRef
Li, X.J.[Xiao-Jie],
Fei, X.[Xin],
Yan, Z.[Zhe],
Ren, H.P.[Hong-Ping],
Shi, C.[Canghong],
Zhang, X.[Xian],
Mumtaz, I.[Imran],
Luo, Y.[Yong],
Wu, X.[Xi],
CAGAN: Classifier-augmented generative adversarial networks for
weakly-supervised COVID-19 lung lesion localisation,
IET-CV(18), No. 1, 2024, pp. 1-14.
DOI Link
2403
biomedical MRI, computer graphics, patient diagnosis
BibRef
Xu, Y.[Yang],
Meng, Z.[Zuqiang],
Interpretable vision transformer based on prototype parts for
COVID-19 detection,
IET-IPR(18), No. 7, 2024, pp. 1927-1937.
DOI Link
2405
image classification, medical image processing
BibRef
Liu, F.X.[Fu-Xiang],
Zang, C.[Chen],
Shi, J.Q.[Jun-Qi],
He, W.Y.[Wei-Yu],
Liang, Y.P.[Yu-Peng],
Li, L.[Lei],
An Improved COVID-19 Lung X-Ray Image Classification Algorithm Based on
ConvNeXt Network,
IJIG(24), No. 3, May 2024, pp. 2450036.
DOI Link
2406
BibRef
Sayeed, A.[Abu],
Khansur, N.O.[Nasif Osman],
Srizon, A.Y.[Azmain Yakin],
Faruk, M.F.[Md. Farukuzzaman],
Alyami, S.A.[Salem A.],
Azad, A.[AKM],
Moni, M.A.[Mohammad Ali],
An effective screening of COVID-19 pneumonia by employing chest X-ray
segmentation and attention-based ensembled classification,
IET-IPR(18), No. 9, 2024, pp. 2400-2416.
DOI Link
2407
chest X-ray, DenseNet201, EfficientNetB7, ensemble learning,
segmentation, Xception
BibRef
Ahishali, M.[Mete],
Degerli, A.[Aysen],
Kiranyaz, S.[Serkan],
Hamid, T.[Tahir],
Mazhar, R.[Rashid],
Gabbouj, M.[Moncef],
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for
blind X-ray restoration and COVID-19 classification,
PR(156), 2024, pp. 110765.
Elsevier DOI Code:
WWW Link.
2408
COVID-19 classification, Generative adversarial networks,
Machine learning, X-ray images, X-ray image restoration
BibRef
Guarrasi, V.[Valerio],
Tronchin, L.[Lorenzo],
Albano, D.[Domenico],
Faiella, E.[Eliodoro],
Fazzini, D.[Deborah],
Santucci, D.[Domiziana],
Soda, P.[Paolo],
Multimodal explainability via latent shift applied to COVID-19
stratification,
PR(156), 2024, pp. 110825.
Elsevier DOI
2408
XAI, Multimodal deep learning, Joint fusion, Classification, COVID-19
BibRef
Singh, P.[Pritpal],
Huang, Y.P.[Yo-Ping],
AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT
Scans Analysis,
SMCS(54), No. 10, October 2024, pp. 6218-6229.
IEEE DOI
2410
Clustering algorithms, Kernel, COVID-19, Computed tomography,
Partitioning algorithms, Indexes, Gray-scale,
coronavirus disease 2019 (COVID-19)
BibRef
Kordnoori, S.[Shirin],
Sabeti, M.[Maliheh],
Mostafaei, H.[Hamidreza],
Banihashemi, S.S.A.[Saeed Seyed Agha],
Advances in medical image analysis:
A comprehensive survey of lung infection detection,
IET-IPR(18), No. 13, 2024, pp. 3750-3800.
DOI Link
2411
image classification, image segmentation, lung, medical image processing
BibRef
Delgado, E.[Emilio],
Rodriguez-Echeverria, R.[Roberto],
Fernández-García, A.J.[Antonio Jesús],
Gutiérrez, J.D.[Juan D.],
Suero-Rodrigo, M.Á.[Miguel Ángel],
Advancing precision in medical image segmentation: A performance
analysis of loss functions for COVID-19 lung infection segmentation
in computed tomography images,
IET-IPR(18), No. 13, 2024, pp. 4047-4065.
DOI Link
2411
biomedical imaging, computerized tomography,
image segmentation, medical image processing, radiology, stability
BibRef
Otahola, B.C.[B. Cuevas],
Arriaga-Hernandez, J.,
Castillo, M.M.[M. Morin],
Oliveros, J.O.[J. Oliveros],
3D solid of SARS-CoV-2 viral particles applying Legendre polynomials
from tomography Fourier analysis,
JOSA-A(40), No. 11, November 2023, pp. 1994-2001.
DOI Link
2503
Biomedical imaging, Fourier transforms, Machine learning,
Machine vision, Scanning electron microscopy, Three dimensional reconstruction
BibRef
Dong, A.[Aimei],
Liu, J.[Jian],
Lv, G.H.[Guo-Hua],
Cheng, J.Y.[Jin-Yong],
GLMR-Net: Global-to-local mutually reinforcing network for pneumonia
segmentation and classification,
PR(162), 2025, pp. 111371.
Elsevier DOI
2503
Pneumonia, Segmentation, Classification, Global-to-local,
Mutual reinforcement, CT images
BibRef
Balaha, H.M.[Hossam Magdy],
Elgendy, M.[Mayada],
Alksas, A.[Ahmed],
Shehata, M.[Mohamed],
Alghamdi, N.S.[Norah Saleh],
Taher, F.[Fatma],
Ghazal, M.[Mohammed],
Ghoneim, M.[Mahitab],
Hamed, E.[Eslam],
Sherif, F.[Fatma],
Elgarayhi, A.[Ahmed],
Sallah, M.[Mohammed],
Salem, M.A.[Mohamed Abdelbadie],
Kamal, E.[Elsharawy],
Sandhu, H.[Harpal],
El-Baz, A.[Ayman],
A Neuroimaging Yolov8-Based Cad Framework for Anosmia Grading in
Covid-19,
ICIP24(2951-2956)
IEEE DOI
2411
COVID-19, Deep learning, Solid modeling, Adaptation models, Accuracy,
Robustness, Medical diagnosis, Anosmia, COVID-19,
Fluid-Attenuated Inversion Recovery (FLAIR)
BibRef
Turnbull, R.[Robert],
Mutch, S.[Simon],
Separating lungs in CT scans for improved COVID19 detection,
DEF-AI-MIA24(5216-5222)
IEEE DOI
2410
COVID-19, Training, Computed tomography, Lung, CT Scans, COVID19,
Domain Adaptation
BibRef
Farag, R.[Ramy],
Upadhay, P.[Parth],
Demby's, J.[Jacket],
Gao, Y.X.[Yi-Xiang],
Montoya, K.G.[Katherin Garces],
Tousi, S.M.A.[Seyed Mohamad Ali],
Omotara, G.[Gbenga],
DeSouza, G.[Guilherme],
EfficientNet-SAM: A Novel EffecientNet with Spatial Attention
Mechanism for COVID-19 Detection in Pulmonary CT Scans,
DEF-AI-MIA24(5200-5206)
IEEE DOI
2410
COVID-19, Training, Visualization, Attention mechanisms,
Computed tomography, Pipelines, EfficientNet, Attention Mechanism,
COVID-19 Detection
BibRef
Li, Q.Q.[Qing-Qiu],
Yuan, R.[Runtian],
Hou, J.L.[Jun-Lin],
Xu, J.[Jilan],
Zhang, Y.[Yuejie],
Feng, R.[Rui],
Chen, H.[Hao],
Advancing COVID-19 Detection in 3D CT Scans,
DEF-AI-MIA24(5149-5156)
IEEE DOI
2410
COVID-19, Training, Solid modeling, Visualization, Computational modeling,
Computed tomography, COVID-19 Detection, Transfer Learning
BibRef
Yuan, R.[Runtian],
Li, Q.Q.[Qing-Qiu],
Hou, J.L.[Jun-Lin],
Xu, J.[Jilan],
Zhang, Y.[Yuejie],
Feng, R.[Rui],
Chen, H.[Hao],
Domain Adaptation Using Pseudo Labels for COVID-19 Detection,
DEF-AI-MIA24(5141-5148)
IEEE DOI
2410
COVID-19, Representation learning, Deep learning, Training,
Adaptation models, COVID-19 Detection, Domain Adaptation
BibRef
Pandey, S.[Stuti],
Myers-Dean, J.[Josh],
Reynolds, J.[Jarek],
Gurari, D.[Danna],
Interpreting COVID Lateral Flow Tests' Results with Foundation Models,
DEF-AI-MIA24(4935-4942)
IEEE DOI Code:
WWW Link.
2410
COVID-19, Pregnancy, Visualization, Image segmentation, Pandemics,
Malaria, Life estimation, Lateral Flow Test,
Accessibility
BibRef
Hsu, C.C.[Chih-Chung],
Lee, C.M.[Chia-Ming],
Chiang, Y.F.[Yang Fan],
Chou, Y.S.[Yi-Shiuan],
Jiang, C.Y.[Chih-Yu],
Tai, S.C.[Shen-Chieh],
Tsai, C.H.[Chi-Han],
A Closer Look at Spatial-Slice Features Learning for COVID-19
Detection,
DEF-AI-MIA24(4924-4934)
IEEE DOI Code:
WWW Link.
2410
Representation learning, COVID-19, Training, Adaptation models,
Computed tomography, Computational modeling, COVID-19 Detection,
Kernel-Density-based Slice Sampling
BibRef
Kollias, D.[Dimitrios],
Arsenos, A.[Anastasios],
Kollias, S.[Stefanos],
Domain adaptation, Explainability & Fairness in AI for Medical Image
Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans,
DEF-AI-MIA24(4907-4914)
IEEE DOI
2410
COVID-19, Training, Image analysis, Databases, Computed tomography,
DEF-AI-MIA, COV19-CT-DB, Covid-19 Detection, Domain Adaptation, Medical Imaging
BibRef
Alam, S.S.[Syeda Sadia],
Munna, K.H.[Kawsar Habib],
Rafee, M.M.S.[Md. Mushtaq Shahriyar],
Covid-19 Detection From CT Scan Images Using Deep Learning Models,
ISCV24(1-6)
IEEE DOI
2408
COVID-19, Accuracy, Computer viruses, Pandemics, Computed tomography,
Computational modeling, Lung, COVID-19, Deep learning algorithm,
Image processing
BibRef
Yang, J.B.[Jian-Bo],
Revolutionizing COVID-19 Diagnosis with Swin Transformer: A
Comparative Study on CT Image Attention Analysisand CNN Models
performance,
CVIDL23(1-5)
IEEE DOI
2403
COVID-19, Measurement, Deep learning, Training, Visualization,
Analytical models, Computed tomography, swin transformer,
attention visualization
BibRef
Ahmed, F.[Faisal],
Nuwagira, B.[Brighton],
Torlak, F.[Furkan],
Coskunuzer, B.[Baris],
Topo-CXR: Chest X-ray TB and Pneumonia Screening with Topological
Machine Learning,
CVAMD23(2318-2328)
IEEE DOI
2401
BibRef
Dack, E.[Ethan],
Brigato, L.[Lorenzo],
McMurray, M.[Matthew],
Fontanellaz, M.[Matthias],
Frauenfelder, T.[Thomas],
Hoppe, H.[Hanno],
Exadaktylos, A.[Aristomenis],
Geiser, T.[Thomas],
Funke-Chambour, M.[Manuela],
Christel, A.[Andreas],
Ebner, L.[Lukas],
Mougiakakou, S.[Stavroula],
An Empirical Analysis for Zero-Shot Multi-Label Classification on
COVID-19 CT Scans and Uncurated Reports,
CVAMD23(2626-2635)
IEEE DOI
2401
BibRef
Roberto, G.F.[Guilherme F.],
Pereira, D.C.[Danilo C.],
Martins, A.S.[Alessandro S.],
Tosta, T.A.A.[Thaína A. A.],
Soares, C.[Carlos],
Lumini, A.[Alessandra],
Rozendo, G.B.[Guilherme B.],
Neves, L.A.[Leandro A.],
Nascimento, M.Z.[Marcelo Z.],
Detection of Covid-19 in Chest X-ray Images Using Percolation Features
and Hermite Polynomial Classification,
CIARP23(I:163-177).
Springer DOI
2312
BibRef
Dong, A.[Aimei],
Wang, R.X.[Rui-Xin],
Lv, G.H.[Guo-Hua],
Zhao, G.X.[Gui-Xin],
Zhai, Y.[Yi],
Mix-Net: Automatic Segmentation of COVID-19 CT Images Based on
Parallel Design,
ICIP23(2145-2149)
IEEE DOI
2312
BibRef
Shea, D.E.[Daniel E],
Kulhare, S.[Sourabh],
Millin, R.[Rachel],
Laverriere, Z.[Zohreh],
Mehanian, C.[Courosh],
Delahunt, C.B.[Charles B],
Banik, D.[Dipayan],
Zheng, X.L.[Xin-Liang],
Zhu, M.[Meihua],
Ji, Y.[Ye],
Ostbye, T.[Travis],
Mehanian, M.M.S.[Martha-Marie S],
Uwajeh, A.[Atinuke],
Akinsete, A.M.[Adeseye M],
Wang, F.[Fen],
Horning, M.P.[Matthew P],
Deep Learning Video Classification of Lung Ultrasound Features
Associated with Pneumonia,
DL-UIA23(3103-3112)
IEEE DOI
2309
BibRef
Ali, H.[Hazrat],
Grönlund, C.[Christer],
Shah, Z.[Zubair],
Leveraging GANs for data scarcity of COVID-19: Beyond the hype,
GCV23(659-667)
IEEE DOI
2309
BibRef
Cores, D.[Daniel],
Vila-Blanco, N.[Nicolás],
Mucientes, M.[Manuel],
Carreira, M.J.[María J.],
Few-shot Image Classification for Automatic Covid-19 Diagnosis,
IbPRIA23(545-556).
Springer DOI
2307
BibRef
Galán-Cuenca, A.[Alejandro],
Mirón, M.[Miguel],
Gallego, A.J.[Antonio Javier],
Saval-Calvo, M.[Marcelo],
Pertusa, A.[Antonio],
Inter vs. Intra Domain Study of Covid Chest X-ray Classification with
Imbalanced Datasets,
IbPRIA23(507-519).
Springer DOI
2307
BibRef
Turnbull, R.[Robert],
Using a 3d Resnet for Detecting the Presence and Severity of Covid-19
from CT Scans,
MIA-COVID19D22(663-676).
Springer DOI
2304
BibRef
Kollias, D.[Dimitrios],
Arsenos, A.[Anastasios],
Kollias, S.[Stefanos],
Ai-mia: Covid-19 Detection and Severity Analysis Through Medical
Imaging,
MIA-COVID19D22(677-690).
Springer DOI
2304
BibRef
Bougourzi, F.[Fares],
Distante, C.[Cosimo],
Dornaika, F.[Fadi],
Taleb-Ahmed, A.[Abdelmalik],
CNR-IEMN-CD and CNR-IEMN-CSD Approaches for Covid-19 Detection and
Covid-19 Severity Detection from 3d Ct-scans,
MIA-COVID19D22(593-604).
Springer DOI
2304
BibRef
Berenguer, A.D.[Abel Díaz],
Mukherjee, T.[Tanmoy],
Da, Y.F.[Yi-Fei],
Bossa, M.N.[Matías Nicolás],
Kvasnytsia, M.[Maryna],
Vandemeulebroucke, J.[Jef],
Deligiannis, N.[Nikos],
Sahli, H.[Hichem],
Representation Learning with Information Theory to Detect Covid-19 and
Its Severity,
MIA-COVID19D22(605-620).
Springer DOI
2304
BibRef
Hsu, C.C.[Chih-Chung],
Tsai, C.H.[Chi-Han],
Chen, G.L.[Guan-Lin],
Ma, S.D.[Sin-Di],
Tai, S.C.[Shen-Chieh],
Spatial-slice Feature Learning Using Visual Transformer and Essential
Slices Selection Module for Covid-19 Detection of Ct Scans in the Wild,
MIA-COVID19D22(621-634).
Springer DOI
2304
BibRef
Hou, J.L.[Jun-Lin],
Xu, J.[Jilan],
Zhang, N.[Nan],
Wang, Y.[Yi],
Zhang, Y.[Yuejie],
Zhang, X.B.[Xiao-Bo],
Feng, R.[Rui],
CMC_V2: Towards More Accurate Covid-19 Detection with Discriminative
Video Priors,
MIA-COVID19D22(485-499).
Springer DOI
2304
BibRef
Kienzle, D.[Daniel],
Lorenz, J.[Julian],
Schön, R.[Robin],
Ludwig, K.[Katja],
Lienhart, R.[Rainer],
Covid Detection and Severity Prediction with 3d-convnext and Custom
Pretrainings,
MIA-COVID19D22(500-516).
Springer DOI
2304
BibRef
Tan, W.J.[Wei-Jun],
Yao, Q.[Qi],
Liu, J.F.[Jing-Feng],
Two-stage Covid19 Classification Using Bert Features,
MIA-COVID19D22(517-525).
Springer DOI
2304
BibRef
Zheng, L.[Lilang],
Fang, J.X.[Jia-Xuan],
Tang, X.R.[Xiao-Run],
Li, H.Z.[Han-Zhang],
Fan, J.X.[Jia-Xin],
Wang, T.Y.[Tian-Yi],
Zhou, R.[Rui],
Yan, Z.Y.[Zhao-Yan],
PVT-COV19D: Covid-19 Detection Through Medical Image Classification
Based on Pyramid Vision Transformer,
MIA-COVID19D22(526-536).
Springer DOI
2304
BibRef
Hou, J.L.[Jun-Lin],
Xu, J.[Jilan],
Zhang, N.[Nan],
Zhang, Y.J.[Yue-Jie],
Zhang, X.B.[Xiao-Bo],
Feng, R.[Rui],
Boosting Covid-19 Severity Detection with Infection-aware Contrastive
Mixup Classification,
MIA-COVID19D22(537-551).
Springer DOI
2304
BibRef
Weninger, L.[Leon],
Romanzetti, S.[Sandro],
Ebert, J.[Julia],
Reetz, K.[Kathrin],
Merhof, D.[Dorit],
Harmonization of Diffusion MRI Data Obtained with Multiple Head Coils
Using Hybrid Cnns,
MIA-COVID19D22(385-396).
Springer DOI
2304
BibRef
Nakhli, R.[Ramin],
Darbandsari, A.[Amirali],
Farahani, H.[Hossein],
Bashashati, A.[Ali],
CCRL: Contrastive Cell Representation Learning,
MIA-COVID19D22(397-407).
Springer DOI
2304
BibRef
Zhang, Z.[Zhen],
Guo, D.L.[Da-Lei],
Unsupervised Domain Adaptation Based Automatic COVID-19 CT
Segmentation,
ICIVC22(375-380)
IEEE DOI
2301
COVID-19, Deep learning, Image segmentation, Visualization,
Statistical analysis, Computed tomography, Training data, COVID-19,
unsupervised domain adaptation
BibRef
Tyagi, M.[Mrinal],
Roy, S.[Santanu],
Bansal, V.[Vibhuti],
Custom Weighted Balanced Loss function for Covid 19 Detection from an
Imbalanced CXR Dataset,
ICPR22(2707-2713)
IEEE DOI
2212
COVID-19, Training, Deep learning, Pandemics, Pulmonary diseases, Lung, Entropy
BibRef
Sahoo, P.[Pranab],
Saha, S.[Sriparna],
Mondal, S.[Samrat],
Sharma, N.[Nelson],
COVID-19 Detection from Lung Ultrasound Images using a Fuzzy
Ensemble-based Transfer Learning Technique,
ICPR22(5170-5176)
IEEE DOI
2212
COVID-19, Deep learning, Training, Adaptation models,
Ultrasonic imaging, Transfer learning, Lung
BibRef
Ben-Haim, T.[Tal],
Sofer, R.M.[Ron Moshe],
Ben-Arie, G.[Gal],
Shelef, I.[Ilan],
Raviv, T.R.[Tammy Riklin],
A Deep Ensemble Learning Approach to Lung CT Segmentation for
Covid-19 Severity Assessment,
ICIP22(151-155)
IEEE DOI
2211
COVID-19, Deep learning, Image segmentation, Pathology, Uncertainty,
Computed tomography, Measurement uncertainty, Severity Assessment
BibRef
Bruton, J.,
Wang, H.,
Translated Skip Connections: Expanding the Receptive Fields of Fully
Convolutional Neural Networks,
ICIP22(631-635)
IEEE DOI
2211
COVID-19, Image segmentation, Convolution, Neural networks,
Object segmentation, Benchmark testing, skip connections,
dilated convolution
BibRef
Li, X.[Xin],
Niu, Q.[Qirui],
Zhang, C.Y.[Chun-Yu],
Ding, H.[Hui],
Shang, Y.Y.[Yuan-Yuan],
PGUNeT: Covid-19 CT Image Segmentation Using GAN and Feature Pyramid,
ICIP22(4098-4102)
IEEE DOI
2211
COVID-19, Training, Image segmentation, Image resolution,
Computed tomography, Semantics, Imaging, COVID-19,
Generative adversarial network
BibRef
Wang, J.[Jing],
Li, B.[Bicao],
Huang, J.[Jie],
Wei, M.M.[Miao-Miao],
Song, M.X.[Meng-Xing],
Wang, Z.M.[Zong-Min],
Lisnet: A Covid-19 Lung Infection Segmentation Network Based on Edge
Supervision and Multi-Scale Context Aggregation,
ICIP22(2941-2945)
IEEE DOI
2211
COVID-19, Image segmentation, Image edge detection,
Computed tomography, Lung, Feature extraction, Skin, COVID-19,
Multi-scale context aggregation
BibRef
Bougourzi, F.[Fares],
Distante, C.[Cosimo],
Dornaika, F.[Fadi],
Taleb-Ahmed, A.[Abdelmalik],
Hadid, A.[Abdenour],
ILC-Unet++ for Covid-19 Infection Segmentation,
MIACOVID22(461-472).
Springer DOI
2208
BibRef
Miron, R.[Radu],
Breaban, M.E.[Mihaela Elena],
Revitalizing Regression Tasks Through Modern Training Procedures:
Applications in Medical Image Analysis for Covid-19 Infection
Percentage Estimation,
MIACOVID22(473-482).
Springer DOI
2208
BibRef
Trinh, Q.H.[Quoc-Huy],
Nguyen, M.V.[Minh-Van],
Nguyen-Dinh, T.P.[Thien-Phuc],
Res-Dense Net for 3D Covid Chest CT-Scan Classification,
MIACOVID22(483-495).
Springer DOI
2208
BibRef
Tricarico, D.[Davide],
Chaudhry, H.A.H.[Hafiza Ayesha Hoor],
Fiandrotti, A.[Attilio],
Grangetto, M.[Marco],
Deep Regression by Feature Regularization for COVID-19 Severity
Prediction,
MIACOVID22(496-507).
Springer DOI
2208
BibRef
Spatafora, M.A.N.[Maria Ausilia Napoli],
Ortis, A.[Alessandro],
Battiato, S.[Sebastiano],
Mixup Data Augmentation for COVID-19 Infection Percentage Estimation,
MIACOVID22(508-519).
Springer DOI
2208
BibRef
Chaudhary, S.[Suman],
Yang, W.T.[Wan-Ting],
Qiang, Y.[Yan],
Swin Transformer for COVID-19 Infection Percentage Estimation from
CT-Scans,
MIACOVID22(520-528).
Springer DOI
2208
BibRef
Hsu, C.C.[Chih-Chung],
Dai, S.J.[Sheng-Jay],
Chen, S.N.[Shao-Ning],
COVID-19 Infection Percentage Prediction via Boosted Hierarchical
Vision Transformer,
MIACOVID22(529-535).
Springer DOI
2208
BibRef
Zedda, L.[Luca],
Loddo, A.[Andrea],
di Ruberto, C.[Cecilia],
A Shallow Learning Investigation for COVID-19 Classification,
AIRCAD22(326-337).
Springer DOI
2208
BibRef
El Boujnouni, M.[Mohamed],
A study and identification of COVID-19 viruses using N-grams with
Naďve Bayes, K-Nearest Neighbors, Artificial Neural Networks,
Decision tree and Support Vector Machine,
ISCV22(1-7)
IEEE DOI
2208
COVID-19, Support vector machines, Terminology,
Text categorization, Genomics, Artificial neural networks,
Decision tree and Support Vector Machine
BibRef
Barbano, C.A.[Carlo Alberto],
Tartaglione, E.[Enzo],
Berzovini, C.[Claudio],
Calandri, M.[Marco],
Grangetto, M.[Marco],
A Two-Step Radiologist-Like Approach for Covid-19 Computer-Aided
Diagnosis from Chest X-Ray Images,
CIAP22(I:173-184).
Springer DOI
2205
BibRef
Vidal, P.L.[Plácido L.],
de Moura, J.[Joaquim],
Novo, J.[Jorge],
Ortega, M.[Marcos],
Pulmonary-Restricted COVID-19 Informative Visual Screening Using Chest
X-ray Images from Portable Devices,
CIAP22(I:65-76).
Springer DOI
2205
BibRef
Zhu, X.Y.[Xiao-Yu],
Chen, J.[Jeffrey],
Zeng, X.[Xiangrui],
Liang, J.W.[Jun-Wei],
Li, C.Q.[Cheng-Qi],
Liu, S.[Sinuo],
Behpour, S.[Sima],
Xu, M.[Min],
Weakly Supervised 3D Semantic Segmentation Using Cross-Image
Consensus and Inter-Voxel Affinity Relations,
ICCV21(2814-2824)
IEEE DOI
2203
COVID-19, Image segmentation, Solid modeling, Microscopy, Semantics,
Integral equations, Medical, biological, and cell microscopy,
grouping and shape
BibRef
Shu, M.[Michelle],
Bowen, R.S.[Richard Strong],
Herrmann, C.[Charles],
Qi, G.[Gengmo],
Santacatterina, M.[Michele],
Zabih, R.[Ramin],
Deep survival analysis with longitudinal X-rays for COVID-19,
ICCV21(4026-4035)
IEEE DOI
2203
COVID-19, Deep learning, Hospitals, Sociology, Neural networks,
Imaging, X-rays, Medical, biological, and cell microscopy,
BibRef
Allaouzi, I.,
Benamrou, B.,
Allaouzi, A.,
Ouardouz, M.,
Ben Ahmed, M.,
AI_COVID: Automatic Diagnosis of Covid-19 Using Frontal Chest X-ray
Image,
SmartCityApp21(57-63).
DOI Link
2201
BibRef
Laurini, E.,
Rotilio, M.,
de Berardinis, P.,
Vittorini, P.,
Cucchiella, F.,
di Stefano, G.,
Ferri, G.,
Stornelli, V.,
Tobia, L.,
Coflex: Flexible Bracelet Anti Covid-19 to Protect Construction Workers,
SmartCity21(63-68).
DOI Link
2201
BibRef
Bosowski, P.[Piotr],
Bosowska, J.[Joanna],
Nalepa, J.[Jakub],
Evolving Deep Ensembles for Detecting Covid-19 In Chest X-Rays,
ICIP21(3772-3776)
IEEE DOI
2201
COVID-19, Measurement, Deep learning, Pandemics, Image processing,
Predictive models, Inference algorithms, COVID-19 detection, X-ray,
genetic algorithm
BibRef
Le, N.[Ngan],
Sorensen, J.[James],
Bui, T.D.[Toan Duc],
Choudhary, A.[Arabinda],
Luu, K.[Khoa],
Nguyen, H.[Hien],
PairFlow: Enhancing Portable Chest X-Ray By Flow-Based Deformation
for Covid-19 Diagnosing,
ICIP21(215-219)
IEEE DOI
2201
COVID-19, Deep learning, Image quality, Pandemics, Image processing,
Neurons, Lung, COVID, Chest Xray, Enhancement,
Flow-based Deformation
BibRef
Altaf, F.[Fouzia],
Islam, S.M.S.[Syed M.S.],
Janjua, N.K.[Naeem K.],
Akhtar, N.[Naveed],
Boosting Deep Transfer Learning for Covid-19 Classification,
ICIP21(210-214)
IEEE DOI
2201
COVID-19, Deep learning, Computed tomography, Image processing,
Computational modeling, Transfer learning, COVID-19, Deep Learning,
Sparse representation.
BibRef
Cao, J.W.[Jia-Wang],
Jiang, L.[Lulu],
Hou, J.L.[Jun-Lin],
Jiang, L.Q.[Long-Quan],
Zhao, R.[Ruiwei],
Shi, W.Y.[Wei-Ya],
Shan, F.[Fei],
Feng, R.[Rui],
Exploiting Deep Cross-Slice Features from CT Images for Multi-Class
Pneumonia Classification,
ICIP21(205-209)
IEEE DOI
2201
COVID-19, Deep learning, Computed tomography, Pulmonary diseases,
Image processing, Imaging, COVID-19, CT image, cross-slice features,
context-aware Bi-LSTM
BibRef
Gomes, D.P.S.[Douglas P. S.],
Ulhaq, A.[Anwaar],
Paul, M.[Manoranjan],
Horry, M.J.[Michael J.],
Chakraborty, S.[Subrata],
Saha, M.[Manash],
Debnath, T.[Tanmoy],
Rahaman, D.M.M.[D.M. Motiur],
Features of ICU Admission In X-Ray Images of Covid-19 Patients,
ICIP21(200-204)
IEEE DOI
2201
COVID-19, Image segmentation, Semantics, Lung, X-rays,
Feature extraction, Covid-19, deep learning, ICU, severity, X-ray
BibRef
Perera, S.[Shehan],
Adhikari, S.[Srikar],
Yilmaz, A.[Alper],
Pocformer: A Lightweight Transformer Architecture for Detection of
Covid-19 Using Point of Care Ultrasound,
ICIP21(195-199)
IEEE DOI
2201
COVID-19, Deep learning, Ultrasonic imaging, Image processing,
Point of care, Lung, Ultrasound, Deep Learning,
Covid-19 Diagnosis, Transformer Networks
BibRef
Yang, H.[Han],
Zhang, M.[Mengke],
Shen, L.[Lu],
Wang, Q.[Qiuli],
Chen, W.Q.[Wan-Qiu],
Liu, C.[Chen],
Hong, M.J.[Min-Jian],
MMFC: Multi-Modal Fusion Cascade Framework for Covid-19 Disease
Course Classification,
ICIP21(190-194)
IEEE DOI
2201
COVID-19, Visualization, Sensitivity, Protocols, Pandemics, Hospitals,
Computed tomography, COVID-19, Course of Disease, Multi-Modal,
Computed Tomography
BibRef
Degerli, A.[Aysen],
Kiranyaz, S.[Serkan],
Chowdhury, M.E.H.[Muhammad E. H.],
Gabbouj, M.[Moncef],
Osegnet: Operational Segmentation Network for Covid-19 Detection
Using Chest X-Ray Images,
ICIP22(2306-2310)
IEEE DOI
2211
COVID-19, Training, Image segmentation, Sensitivity,
Machine learning algorithms, Computer network reliability,
Deep Learning
BibRef
Degerli, A.[Aysen],
Ahishali, M.[Mete],
Kiranyaz, S.[Serkan],
Chowdhury, M.E.H.[Muhammad E. H.],
Gabbouj, M.[Moncef],
Reliable Covid-19 Detection using Chest X-Ray Images,
ICIP21(185-189)
IEEE DOI
2201
COVID-19, Analytical models, Sensitivity,
Machine learning algorithms, Computer network reliability,
Deep Learning
BibRef
Afshar, P.[Parnian],
Heidarian, S.[Shahin],
Naderkhani, F.[Farnoosh],
Rafiee, M.J.[Moezedin Javad],
Oikonomou, A.[Anastasia],
Plataniotis, K.N.[Konstantinos N.],
Mohammadi, A.[Arash],
Hybrid Deep Learning Model for Diagnosis of Covid-19 Using Ct Scans
and Clinical/Demographic Data,
ICIP21(180-184)
IEEE DOI
2201
COVID-19, Deep learning, Sensitivity, Computed tomography, Lung,
Predictive models, Tools, COVID-19 Identification,
Hybrid Model
BibRef
Liu, X.H.[Xiao-Hong],
Wang, K.[Kai],
Chen, T.[Ting],
Deep Active Learning for Fibrosis Segmentation of Chest CT Scans from
Covid-19 Patients,
ICIP21(175-179)
IEEE DOI
2201
COVID-19, Image segmentation, Uncertainty, Annotations,
Computed tomography, Pulmonary diseases, Redundancy, chest CT,
active learning
BibRef
Panicker, M.R.[Mahesh Raveendranatha],
Chen, Y.T.[Yale Tung],
Gayathri, M.,
Madhavanunni, N.A.,
Narayan, K.V.[Kiran Vishnu],
Kesavadas, C.,
Vinod, A.P.,
Employing Acoustic Features To Aid Neural Networks Towards Platform
Agnostic Learning In Lung Ultrasound Imaging,
ICIP21(170-174)
IEEE DOI
2201
COVID-19, Ultrasonic imaging, Neural networks, Lung, Imaging, Tools,
Feature extraction, COVID-19, Lung Ultrasound, Pleura Detection,
Neural Networks
BibRef
Altaf, F.[Fouzia],
Islam, S.M.S.[Syed M.S.],
Akhtar, N.[Naveed],
Resetting the baseline: CT-based COVID-19 diagnosis with Deep
Transfer Learning is not as accurate as widely thought,
DICTA21(01-08)
IEEE DOI
2201
COVID-19, Training, Performance evaluation, Visualization,
Systematics, Sensitivity, Computed tomography, Deep learning,
medical imaging
BibRef
Sebdani, A.M.[Abbas Mazrouei],
Mostafavi, A.[Amir],
Medical Image Processing and Deep Learning to Diagnose COVID-19 with
CT Images,
IPRIA21(1-6)
IEEE DOI
2201
COVID-19, Support vector machines, Image analysis,
Computed tomography, Lung, Artificial neural networks,
CT scan image
BibRef
Prabhushankar, M.[Mohit],
AlRegib, G.[Ghassan],
Extracting Causal Visual Features for Limited Label Classification,
ICIP21(3697-3701)
IEEE DOI
2201
Measurement, COVID-19, Visualization, Image coding,
Computed tomography, Neural networks, Visual Causality,
Causal metrics
BibRef
Dehzangi, O.[Omid],
Jeihouni, P.[Paria],
Finomore, V.[Victor],
Rezai, A.[Ali],
Physiological Monitoring of Front-Line Caregivers for CV-19 Symptoms:
Multi-Resolution Analysis & Convolutional-Recurrent Networks,
ICIP21(250-254)
IEEE DOI
2201
COVID-19, Deep learning, Recurrent neural networks, Sociology,
Decision making, Convolutional neural networks,
RNN.
BibRef
Wang, D.D.[Da-Dong],
Arzhaeva, Y.[Yulia],
Devnath, L.[Liton],
Qiao, M.Y.[Mao-Ying],
Amirgholipour, S.[Saeed],
Liao, Q.Y.[Qi-Yu],
McBean, R.[Rhiannon],
Hillhouse, J.[James],
Luo, S.[Suhuai],
Meredith, D.[David],
Newbigin, K.[Katrina],
Yates, D.[Deborah],
Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded
Learning with Real and Synthetic Radiographs,
DICTA20(1-6)
IEEE DOI
2201
Training, Sensitivity, Computational modeling, Pulmonary diseases,
Lung, X-rays, Diagnostic radiography, pneumoconiosis, deep learning,
black lung
BibRef
Teli, M.N.[Mohammad Nayeem],
TeliNet: Classifying CT scan images for COVID-19 diagnosis,
MIA-COVID19D21(496-502)
IEEE DOI
2112
COVID-19, Convolution, Computed tomography,
Machine learning, Benchmark testing, Reproducibility of results
BibRef
Anwar, T.[Talha],
COVID19 Diagnosis using AutoML from 3D CT scans,
MIA-COVID19D21(503-507)
IEEE DOI
2112
COVID-19, Solid modeling,
Computed tomography, Computational modeling,
Predictive models
BibRef
Liang, S.[Shuang],
Zhang, W.[Weicun],
Gu, Y.[Yu],
A hybrid and fast deep learning framework for Covid-19 detection via
3D Chest CT Images,
MIA-COVID19D21(508-512)
IEEE DOI
2112
COVID-19, Deep learning, Solid modeling,
Computed tomography, Lead, Network architecture
BibRef
Zhang, L.[Lei],
Wen, Y.[Yan],
A transformer-based framework for automatic COVID19 diagnosis in
chest CTs,
MIA-COVID19D21(513-518)
IEEE DOI
2112
COVID-19, Image segmentation,
Computed tomography, Lung, Predictive models, Transformers
BibRef
Ayyar, M.P.[Meghna P],
Benois-Pineau, J.[Jenny],
Zemmari, A.[Akka],
A Hierarchical Classification System for the Detection of Covid-19
from Chest X-Ray Images,
MIA-COVID19D21(519-528)
IEEE DOI
2112
COVID-19, Training, Deep learning, Sensitivity, Pulmonary diseases,
Computed tomography, Pipelines
BibRef
Miron, R.[Radu],
Moisii, C.[Cosmin],
Dinu, S.[Sergiu],
Breaban, M.E.[Mihaela Elena],
Evaluating volumetric and slice-based approaches for COVID-19
detection in chest CTs,
MIA-COVID19D21(529-536)
IEEE DOI
2112
COVID-19, Training, Deep learning,
Computed tomography, Aggregates
BibRef
Kollias, D.[Dimitrios],
Arsenos, A.[Anastasios],
Soukissian, L.[Levon],
Kollias, S.[Stefanos],
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis,
MIA-COVID19D21(537-544)
IEEE DOI
2112
COVID-19, Deep learning, Training, Data privacy, Image analysis,
Databases, Computed tomography
BibRef
Rao, A.[Adrit],
Park, J.[Jongchan],
Aalami, O.[Oliver],
The Value of Visual Attention for COVID-19 Classification in CT Scans,
MIA-COVID19D21(433-438)
IEEE DOI
2112
COVID-19, Heating systems, Deep learning, Visualization,
Computed tomography, Convolutional neural networks
BibRef
Rundo, F.,
Genovese, A.,
Leotta, R.,
Scotti, F.,
Piuri, V.,
Battiato, S.,
Advanced 3D Deep Non-Local Embedded System for Self-Augmented
X-Ray-based COVID-19 Assessment,
MIA-COVID19D21(423-432)
IEEE DOI
2112
COVID-19, Training, Sensitivity,
Neural networks, Reinforcement learning, Radiology
BibRef
Tan, W.J.[Wei-Jun],
Liu, J.F.[Jing-Feng],
A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From
CT-Scan Images,
MIA-COVID19D21(439-445)
IEEE DOI
2112
COVID-19, Training, Image segmentation,
Codes, Conferences
BibRef
Gil, D.[Debora],
Baeza, S.[Sonia],
Sanchez, C.[Carles],
Torres, G.[Guillermo],
García-Olivé, I.[Ignasi],
Moragas, G.[Gloria],
Deportós, J.[Jordi],
Salcedo, M.[Maite],
Rosell, A.[Antoni],
Intelligent Radiomic Analysis of Q-SPECT/CT images to optimize
pulmonary embolism diagnosis in COVID-19 patients,
MIA-COVID19D21(446-453)
IEEE DOI
2112
COVID-19, Pulmonary diseases, Lung,
Receivers, Feature extraction
BibRef
Hou, J.L.[Jun-Lin],
Xu, J.[Jilan],
Feng, R.[Rui],
Zhang, Y.[Yuejie],
Shan, F.[Fei],
Shi, W.Y.[Wei-Ya],
CMC-COV19D: Contrastive Mixup Classification for COVID-19 Diagnosis,
MIA-COVID19D21(454-461)
IEEE DOI
2112
COVID-19, Training, Deep learning, Epidemics, Image analysis,
Databases, Pulmonary diseases
BibRef
Amer, A.[Alyaa],
Ye, X.[Xujiong],
Janan, F.[Faraz],
Residual Dilated U-net For The Segmentation Of COVID-19 Infection
From CT Images,
MIA-COVID19D21(462-470)
IEEE DOI
2112
COVID-19, Deep learning, Training, Image segmentation, Convolution,
Shape, Computed tomography
BibRef
Chen, G.L.[Guan-Lin],
Hsu, C.C.[Chih-Chung],
Wu, M.H.[Mei-Hsuan],
Adaptive Distribution Learning with Statistical Hypothesis Testing
for COVID-19 CT Scan Classification,
MIA-COVID19D21(471-479)
IEEE DOI
2112
COVID-19, Deep learning, Visualization, Statistical analysis,
Computed tomography, Transformers
BibRef
Tartaglione, E.[Enzo],
Barbano, C.A.[Carlo Alberto],
Grangetto, M.[Marco],
EnD: Entangling and Disentangling deep representations for bias
correction,
CVPR21(13503-13512)
IEEE DOI
2111
Training, COVID-19, Radiography, Deep learning,
Training data, Data models
BibRef
Simon, M.[Mylene],
Schaub, N.J.[Nicholas J.],
Yu, S.[Sunny],
Ouladi, M.[Mohamed],
Nagarajan, J.[Jayapriya],
Bayankaram, S.P.[Sudharsan Prativadi],
Bajcsy, P.[Peter],
Hotaling, N.[Nathan],
Quantifying Variability in Microscopy Image Analyses for COVID-19
Drug Discovery,
CVMI21(3796-3804)
IEEE DOI
2109
Drugs, COVID-19, Coordinate measuring machines,
Thresholding (Imaging), Microscopy, Measurement uncertainty, Hardware
BibRef
Campos, M.S.R.[Michael Stiven Ramirez],
Bautista, S.S.[Santiago Saavedra],
Guerrero, J.V.A.[Jose Vicente Alzate],
Suárez, S.C.[Sandra Cancino],
López, J.M.L.[Juan M. López],
COVID-19 Related Pneumonia Detection in Lung Ultrasound,
MCPR21(316-324).
Springer DOI
2108
BibRef
Laradji, I.[Issam],
Rodriguez, P.[Pau],
Mańas, O.[Oscar],
Lensink, K.[Keegan],
Law, M.[Marco],
Kurzman, L.[Lironne],
Parker, W.[William],
Vázquez, D.[David],
Nowrouzezahrai, D.[Derek],
A Weakly Supervised Consistency-based Learning Method for COVID-19
Segmentation in CT Images,
WACV21(2452-2461)
IEEE DOI
2106
COVID-19, Learning systems, Image segmentation,
Annotations, Computed tomography
BibRef
Calderon-Ramirez, S.[Saul],
Giri, R.[Raghvendra],
Yang, S.X.[Sheng-Xiang],
Moemeni, A.[Armaghan],
Umańa, M.[Mario],
Elizondo, D.[David],
Torrents-Barrena, J.[Jordina],
Molina-Cabello, M.A.[Miguel A.],
Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with
Mix Match for Covid-19 Detection Using Chest X-ray Images,
ICPR21(5294-5301)
IEEE DOI
2105
Deep learning, COVID-19, Training, Solid modeling, Scalability, X-rays,
Semisupervised learning, Semi-supervised Deep Learning,
Computer Aided Diagnosis
BibRef
Prasad, S.[Shitala],
Lin, D.Y.[Dong-Yun],
Li, Y.Q.[Yi-Qun],
Sheng, D.[Dong],
Min, O.Z.[Oo Zaw],
Rethinking of Deep Models Parameters with Respect to Data
Distribution,
ICPR21(8562-8569)
IEEE DOI
2105
Training, Deep learning, COVID-19, Image segmentation, Annotations,
Object detection, Data models
BibRef
Xu, R.[Rui],
Wang, Y.[Yi],
Liu, T.T.[Tian-Tian],
Ye, X.C.[Xin-Chen],
Lin, L.[Lin],
Chen, Y.W.[Yen-Wei],
Kido, S.[Shoji],
Tomiyama, N.[Noriyuki],
BG-Net: Boundary-Guided Network for Lung Segmentation on Clinical CT
Images,
ICPR21(8782-8788)
IEEE DOI
2105
COVID-19, Deep learning, Image segmentation, Hospitals,
Computed tomography, Pulmonary diseases, Lung, Lung Segmentation,
CT Images
BibRef
Xu, R.[Rui],
Cao, X.[Xiao],
Wang, Y.F.[Yu-Feng],
Chen, Y.W.[Yen-Wei],
Ye, X.C.[Xin-Chen],
Lin, L.[Lin],
Zhu, W.C.[Wen-Chao],
Chen, C.[Chao],
Xu, F.Y.[Fang-Yi],
Zhou, Y.[Yong],
Hu, H.J.[Hong-Jie],
Kido, S.[Shoji],
Tomiyama, N.[Noriyuki],
Unsupervised Detection of Pulmonary Opacities for Computer-Aided
Diagnosis of COVID-19 on CT Images,
ICPR21(9007-9014)
IEEE DOI
2105
COVID-19, Solid modeling, Computed tomography, Pulmonary diseases,
Lung, Tools, Feature extraction, COVID-19, CT images,
Computer-aided diagnosis
BibRef
Buongiorno, R.[Rossana],
Germanese, D.[Danila],
Romei, C.[Chiara],
Tavanti, L.[Laura],
de Liperi, A.[Annalisa],
Colantonio, S.[Sara],
UIP-Net: A Decoder-encoder CNN for the Detection and Quantification of
Usual Interstitial Pneumoniae Pattern in Lung CT Scan Images,
AIHA20(389-405).
Springer DOI
2103
BibRef
Yazdekhasty, P.[Parham],
Zindari, A.[Ali],
Nabizadeh-ShahreBabak, Z.[Zahra],
Roshandel, R.[Roshanak],
Khadivi, P.[Pejman],
Karimi, N.[Nader],
Samavi, S.[Shadrokh],
Bifurcated Autoencoder for Segmentation of Covid-19 Infected Regions in
Ct Images,
DLPR20(597-607).
Springer DOI
2103
BibRef
Chiari, M.[Mattia],
Gerevini, A.E.[Alfonso E.],
Maroldi, R.[Roberto],
Olivato, M.[Matteo],
Putelli, L.[Luca],
Serina, I.[Ivan],
Length of Stay Prediction for Northern Italy Covid-19 Patients Based on
Lab Tests and X-ray Data,
AIHA20(212-226).
Springer DOI
2103
BibRef
Qjidaa, M.,
Ben-Fares, A.,
Mechbal, Y.,
Amakdouf, H.,
Maaroufi, M.,
Alami, B.,
Qjidaa, H.,
Development of a clinical decision support system for the early
detection of COVID-19 using deep learning based on chest radiographic
images,
ISCV20(1-6)
IEEE DOI
2011
convolutional neural nets, decision support systems,
diagnostic radiography, diseases, feature extraction,
clinical decision support system
BibRef
Qjidaa, M.,
Mechbal, Y.,
Ben-fares, A.,
Amakdouf, H.,
Maaroufi, M.,
Alami, B.,
Qjidaa, H.,
Early detection of COVID19 by deep learning transfer Model for
populations in isolated rural areas,
ISCV20(1-5)
IEEE DOI
2011
decision support systems, diagnostic radiography, diseases,
image classification, learning (artificial intelligence), lung,
rural area
BibRef
Gazzah, S.,
Bencharef, O.,
A Survey on how computer vision can response to urgent need to
contribute in COVID-19 pandemics,
ISCV20(1-5)
IEEE DOI
2011
computerised tomography, diagnostic radiography,
diseases, epidemics, learning (artificial intelligence),
CNN.
BibRef
Gabruseva, T.,
Poplavskiy, D.,
Kalinin, A.,
Deep Learning for Automatic Pneumonia Detection,
WiCV20(1436-1443)
IEEE DOI
2008
Lung, Diseases, Training, X-ray imaging, Diagnostic radiography,
Measurement, Predictive models
BibRef
Sousa, G.G.B.[Gabriel Garcez Barros],
Fernandes, V.R.M.[Vandécia Rejane Monteiro],
de Paiva, A.C.[Anselmo Cardoso],
Optimized Deep Learning Architecture for the Diagnosis of Pneumonia
Through Chest X-Rays,
ICIAR19(II:353-361).
Springer DOI
1909
BibRef
Murray, V.[Victor],
Pattichis, M.S.[Marios S.],
Davis, H.[Herbert],
Barriga, E.S.[Eduardo S.],
Soliz, P.[Peter],
Multiscale AM-FM analysis of pneumoconiosis x-ray images,
ICIP09(4201-4204).
IEEE DOI
0911
BibRef
Tong, X.[Xiaoou],
Tao, D.C.[Da-Cheng],
Antonio, G.E.,
Texture classification of SARS infected region in radiographic image,
ICIP04(V: 2941-2944).
IEEE DOI
0505
BibRef
Pattichis, M.S.,
Pattichis, C.S.,
Christodoulou, C.I.,
James, D.,
Ketai, L.,
Soliz, P.,
A screening system for the assessment of opacity profusion in chest
radiographs of miners with pneumoconiosis,
Southwest02(130-133).
IEEE Top Reference.
0208
BibRef
Chen, X.[Xuan],
Hasegawa, J.I.,
Toriwaki, J.I.,
Quantitative diagnosis of pneumoconiosis based on recognition of small
rounded opacities in chest X-ray images,
ICPR88(I: 462-464).
IEEE DOI
8811
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Emphysema, Lung Analysis .