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Lung, Computed tomography, Diseases, Hospitals, Radiology,
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Lung, Computed tomography, Feature extraction, Hospitals, Testing,
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Computed tomography, Lung, Lesions, Machine learning,
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Computed tomography, Image segmentation, Lung, Training, Data models,
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Computed tomography, Solid modeling, Lung,
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Noise measurement, Image segmentation, Lesions, Lung, Training,
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Computed tomography, Lung, Image segmentation, Diseases, Convolution,
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Image segmentation, Lung, Ultrasonic imaging, Task analysis,
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2008
Lung, Diseases, Image segmentation, Training, Neural networks,
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Computed tomography, Diseases, Lung,
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Lung, Diseases, Computed tomography, Lesions, Task analysis,
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COVID-19 pandemic, X-ray images, Deep learning, Capsule network
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COVID-19, Computer-aided detection (CAD),
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Deep Learning, CNN, Pattern Recognition, COVID-19
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Chest X-Ray, Pneumonia, VGG19 Architecture, Deep-Learning,
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image classification, machine learning, pneumonia,
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bacterial pneumonia, COVID-19, CT, LightGBM, radiomics
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2102
classification, coronavirus, COVID-19, feature extraction,
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attention mechanism, COVID-19, CT, deep learning,
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artificial intelligence, computed tomography image,
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COVID-19, Image reconstruction, Anomaly detection, Memory modules,
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Diseases, Lung, COVID-19, X-rays, Anomaly detection, Viruses (medical),
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COVID-19, Computed tomography, Image segmentation, Sensitivity,
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Corona-virus Ddisease (COVID-19),
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2103
Computed tomography, X-ray, COVID-19, Deep learning,
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COVID-19 diagnosis, Multi-shot learning, Contrastive loss,
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COVID-19 diagnosis, Few-shot learning, Contrastive learning, Chest CT images
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COVID-19, CT, Severity assessment, Lung lobe segmentation,
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2105
capsule network, convolutional neural network, COVID-19,
decision support system, deep learning, visual geometry group, X-ray
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Ekici, F.[Faysal],
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Automatic detection and localization of COVID-19 pneumonia using
axial computed tomography images and deep convolutional neural
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IJIST(31), No. 2, 2021, pp. 509-524.
DOI Link
2105
classification, computer-aided diagnosis,
convolutional neural networks, coronavirus, COVID-19,
radiology
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Khan, M.A.[Murtaza Ali],
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IJIST(31), No. 2, 2021, pp. 499-508.
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2105
artificial intelligence, chest X-ray radiograph, COVID-19,
feature descriptors, medical image processing
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Dhaka, V.S.[Vijaypal Singh],
Rani, G.[Geeta],
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DOI Link
2105
CNN model, Corona, COVID-19, deep learning, global pandemic, X-ray
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Soliman, M.[Mona],
Hassanien, A.E.[Aboul Ella],
COVID-19 vs influenza viruses: A cockroach optimized deep neural
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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
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Dash, T.K.[Tusar Kanti],
Mishra, S.[Soumya],
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PR(117), 2021, pp. 107999.
Elsevier DOI
2106
Bio-inspired computing, COVID19, Speech signal
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PR(119), 2021, pp. 108055.
Elsevier DOI
2106
Deep learning, Attention, Coronavirus, X-ray images, Multi-scale
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COVID-index: A texture-based approach to classifying lung lesions
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PR(119), 2021, pp. 108083.
Elsevier DOI
2106
COVID-19, Computed tomography, 3D texture analysis, Phylogenetic diversity
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Irmak, E.[Emrah],
COVID-19 disease severity assessment using CNN model,
IET-IPR(15), No. 8, 2021, pp. 1814-1824.
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COVID-19 discrimination framework for X-ray images by considering
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SP:IC(97), 2021, pp. 116359.
Elsevier DOI
2107
Binary categorization, Chaotic, Coronavirus, Framework design,
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Chen, Y.[Yang],
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PR(119), 2021, pp. 108109.
Elsevier DOI
2108
COVID-19, Convolutional neural network, Segmentation,
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Saleh, A.I.[Ahmed I.],
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PR(119), 2021, pp. 108110.
Elsevier DOI
2108
COVID-19, Classification, NB, Feature selection, Wrapper,
Optimization, Particle swarm
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Hu, J.L.[Jin-Long],
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A Triplet network framework based automatic assessment of simulation
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PR(119), 2021, pp. 108060.
Elsevier DOI
2108
Simulation quality assessment,
Respiratory droplet propagation, Triplet network, Attentive temporal pooling
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Sousa, O.[Orrana],
Magalhăes, D.[Deborah],
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PR(119), 2021, pp. 108081.
Elsevier DOI
2108
COVID-19, X-ray, Deep learning, Pre-processing
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Zhao, C.[Chen],
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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
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PR(119), 2021, pp. 108071.
Elsevier DOI
2108
COVID-19, Chest CT, Pulmonary parenchyma segmentation,
Deep learning, 3D V-Net
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Zhu, J.W.[Jing-Wen],
Tian, Y.Z.[Yu-Zhen],
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IJGI(10), No. 7, 2021, pp. xx-yy.
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Cho, Y.[Yongwon],
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Kim, M.J.[Min Ju],
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Deep convolution neural networks to differentiate between COVID-19
and other pulmonary abnormalities on chest radiographs: Evaluation
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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],
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Jain, S.[Sahil],
Baranwal, M.[Manoj],
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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
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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.[GuangYu],
Zhao, S.G.[Shu-Guang],
COVSeg-NET: A deep convolution neural network for COVID-19 lung CT
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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],
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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.[Yaozong],
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.[Yaozong],
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.[Jinhong],
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.[Haigen],
Shen, L.[Leizhao],
Guan, Q.[Qiu],
Li, X.X.[Xiao-Xin],
Zhou, Q.[Qianwei],
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,
Computer architecture, 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.[Chaodong],
Zeng, Z.[Zhenhuan],
Xiao, L.[Leyi],
Qu, X.[Xilong],
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
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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
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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
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Elsevier DOI
2209
COVID-19, Infection segmentation, CT image, Deep learning,
Convolutional neural networks
BibRef
Sanchez, K.[Karen],
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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.],
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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.[Liting],
Tan, W.J.[Wen-Jun],
Xu, L.[Lisheng],
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
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
Computer vision, COVID-19, Human analysis, Masked faces, Survey
BibRef
Liu, Y.[Yanbei],
Li, H.[Henan],
Luo, T.[Tao],
Zhang, C.Q.[Chang-Qing],
Xiao, Z.[Zhitao],
Wei, Y.[Ying],
Gao, Y.[Yaozong],
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
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.[Zongmin],
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.[Wanting],
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, Computer architecture,
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
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Emphysema, Lung Analysis .