Pneumonia, Lung Analysis, Flu, COVID

Chapter Contents (Back)
Pneumonia. COVID. Lungs. Medical, Applications.
See also GIS: GIS for Medical Applications, COVID Specific Tracking, Spread, Analysis.

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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

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Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia,
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Lung, Computed tomography, Diseases, Hospitals, Radiology, Image segmentation, COVID-19, COVID-19 Diagnosis, Online Attention, Dual Sampling Strategy BibRef

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Lung, Computed tomography, Feature extraction, Hospitals, Testing, COVID-19, COVID-19, Pneumonia, Chest computed tomography (CT), Multi-view representation learning BibRef

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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.,
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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,
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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.
Noise measurement, Image segmentation, Lesions, Lung, Training, COVID-19, COVID-19, convolutional neural network, noisy label, pneumonia BibRef

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Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans,
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Computed tomography, Lung, Image segmentation, Diseases, Convolution, Neural networks, Training, COVID-19, Computed Tomography, COVID-19, Segmentation BibRef

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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.
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.
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.],
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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,
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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,
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Lung, Diseases, Computed tomography, Lesions, Task analysis, Image segmentation, Biomedical imaging, COVID-19, COVID-19, deep attention learning BibRef

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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,
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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],
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Polsinelli, M.[Matteo], Cinque, L.[Luigi], Placidi, G.[Giuseppe],
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Elsevier DOI 2012
Deep Learning, CNN, Pattern Recognition, COVID-19 BibRef

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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.[Yichu], 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.
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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.
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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., Zhang, H., Wang, Y., Yang, Y., Zhou, X., Wu, Q.M.J.,
MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection,
MedImg(40), No. 3, March 2021, pp. 1032-1041.
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.
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.,
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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,
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Elsevier DOI 2103
Corona-virus Ddisease (COVID-19), Computer-Aaided Ddetection (CAD), COVID-19 lesion, 3D Visualization BibRef

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Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19,
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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,
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Elsevier DOI 2103
COVID-19 diagnosis, Multi-shot learning, Contrastive loss, CXR images, Siamese network BibRef

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Elsevier DOI 2103
COVID-19 diagnosis, Few-shot learning, Contrastive learning, Chest CT images BibRef

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Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images,
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Elsevier DOI 2103
COVID-19, CT, Severity assessment, Lung lobe segmentation, Multi-instance learning BibRef

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Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping,
IJGI(10), No. 2, 2021, pp. xx-yy.
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Upadhyay, K.[Kamini], Agrawal, M.[Monika], Deepak, D.[Desh],
Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images,
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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.[Yuqi], 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

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.[Jinlong], Xu, S.[Songhua], 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

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.[GuangYu], 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

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.
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.[Lisong], Xu, X.Y.[Xiang-Yang], Li, T.[Tianyi], Guo, Y.[Yichen], 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.
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.
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.[Jinshan], 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

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.
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.[Yuedong], Shen, J.[Jun], Zha, Y.[Yunfei],
A coarse-refine segmentation network for COVID-19 CT images,
IET-IPR(16), No. 2, 2022, pp. 333-343.
DOI Link 2201

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

Allaouzi, I., Benamrou, B., Allaouzi, A., Ouardouz, M., Ben Ahmed, M.,
AI_COVID: Automatic Diagnosis of Covid-19 Using Frontal Chest X-ray Image,
DOI Link 2201

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,
DOI Link 2201

Bosowski, P.[Piotr], Bosowska, J.[Joanna], Nalepa, J.[Jakub],
Evolving Deep Ensembles for Detecting Covid-19 In Chest X-Rays,
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,
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,
COVID-19, Deep learning, Computed tomography, Image processing, Computational modeling, Transfer learning, COVID-19, Deep Learning, Sparse representation. BibRef

Cao, J.[Jiawang], Jiang, L.[Lulu], Hou, J.L.[Jun-Lin], Jiang, L.Q.[Long-Quan], Zhao, R.[Ruiwei], Shi, W.[Weiya], Shan, F.[Fei], Feng, R.[Rui],
Exploiting Deep Cross-Slice Features from CT Images for Multi-Class Pneumonia Classification,
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,
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,
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,
COVID-19, Visualization, Sensitivity, Protocols, Pandemics, Hospitals, Computed tomography, COVID-19, Course of Disease, Multi-Modal, Computed Tomography 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,
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,
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,
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,
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,
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,
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,
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,
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.[Qiyu], 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,
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,
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,
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,
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,
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,
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,
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,
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,
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,
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,
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,
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.[Weiya],
CMC-COV19D: Contrastive Mixup Classification for COVID-19 Diagnosis,
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,
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,
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,
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,
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,
Springer DOI 2108

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,
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,
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.[Dongyun], Li, Y.Q.[Yi-Qun], Sheng, D.[Dong], Min, O.Z.[Oo Zaw],
Rethinking of Deep Models Parameters with Respect to Data Distribution,
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.[Xinchen], 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,
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.[Fangyi], 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,
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,
Springer DOI 2103

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,
Springer DOI 2103

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,
Springer DOI 2103

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,
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,
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,
computerised tomography, diagnostic radiography, diseases, epidemics, learning (artificial intelligence), CNN. BibRef

Gabruseva, T., Poplavskiy, D., Kalinin, A.,
Deep Learning for Automatic Pneumonia Detection,
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,
Springer DOI 1909

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,

Tong, X.[Xiaoou], Tao, D.C.[Da-Cheng], Antonio, G.E.,
Texture classification of SARS infected region in radiographic image,
ICIP04(V: 2941-2944).

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,
IEEE Top Reference. 0208

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).

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Emphysema, Lung Analysis .

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