Pneumonia, Lung Analysis, Flu, COVID

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

Pattichis, M.S., Cacoullos, T., Soliz, P.[Peter],
New models for region of interest reader classification analysis in chest radiographs,
PR(42), No. 6, June 2009, pp. 1058-1066.
Elsevier DOI 0902
Region of interest classification; Chest radiographs; ROC analysis; Binary classification; Pneumoconiosis; Modeling biomedical systems; Logic, set theory, and algebra; Mathematical procedures and computer techniques BibRef

Zhao, W.[Wei], Xu, R.[Rui], Hirano, Y.S.[Yasu-Shi], Tachibana, R.[Rie], Kido, S.[Shoji], Suganuma, N.[Narufumi],
Classification of Pneumoconiosis on HRCT Images for Computer-Aided Diagnosis,
IEICE(E96-D), No. 4, April 2013, pp. 836-844.
WWW Link. 1304

Wang, Y.[Ying], Waylen, P.R.[Peter R.], Mao, L.[Liang],
Modeling Properties of Influenza-Like Illness Peak Events with Crossing Theory,
IJGI(3), No. 2, 2014, pp. 764-780.
DOI Link 1407

Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., Shi, F., Yuan, H., Wei, Y., Cao, X., Gao, Y., Wu, D., Wang, Q., Shen, D.,
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia,
MedImg(39), No. 8, August 2020, pp. 2595-2605.
Lung, Computed tomography, Diseases, Hospitals, Radiology, Image segmentation, COVID-19, COVID-19 Diagnosis, Online Attention, Dual Sampling Strategy BibRef

Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., Shen, D.,
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning,
MedImg(39), No. 8, August 2020, pp. 2606-2614.
Lung, Computed tomography, Feature extraction, Hospitals, Testing, COVID-19, COVID-19, Pneumonia, Chest computed tomography (CT), Multi-view representation learning BibRef

Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C.,
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT,
MedImg(39), No. 8, August 2020, pp. 2615-2625.
Computed tomography, Lung, Lesions, Machine learning, Training, Diseases, COVID-19, COVID-19, CT, DeCoVNet BibRef

Fan, D., Zhou, T., Ji, G., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.,
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images,
MedImg(39), No. 8, August 2020, pp. 2626-2637.
Computed tomography, Image segmentation, Lung, Training, Data models, Diseases, X-rays, COVID-19, COVID-19, CT image, infection segmentation, semi-supervised learning BibRef

Zhou, L., Li, Z., Zhou, J., Li, H., Chen, Y., Huang, Y., Xie, D., Zhao, L., Fan, M., Hashmi, S., Abdelkareem, F., Eiada, R., Xiao, X., Li, L., Qiu, Z., Gao, X.,
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis,
MedImg(39), No. 8, August 2020, pp. 2638-2652.
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

Xie, W., Jacobs, C., Charbonnier, J., van Ginneken, B.,
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans,
MedImg(39), No. 8, August 2020, pp. 2664-2675.
Computed tomography, Lung, Image segmentation, Diseases, Convolution, Neural networks, Training, COVID-19, Computed Tomography, COVID-19, Segmentation BibRef

Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldati, G., Rota, P., Passerini, A., van Sloun, R.J.G., Ricci, E., Demi, L.,
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound,
MedImg(39), No. 8, August 2020, pp. 2676-2687.
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.],
The COVID-19 pandemic: What can bioengineers, computer scientists and big data specialists bring to the table,
IJIST(30), No. 3, 2020, pp. 511-512.
DOI Link 2008

Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W.,
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning,
MedImg(39), No. 8, August 2020, pp. 2584-2594.
Computed tomography, Diseases, Lung, Manuals, Medical diagnostic imaging, machine learning BibRef

Wang, J., Bao, Y., Wen, Y., Lu, H., Luo, H., Xiang, Y., Li, X., Liu, C., Qian, D.,
Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images,
MedImg(39), No. 8, August 2020, pp. 2572-2583.
Lung, Diseases, Computed tomography, Lesions, Task analysis, Image segmentation, Biomedical imaging, COVID-19, COVID-19, deep attention learning BibRef

Farhat, H.[Hanan], Sakr, G.E.[George E.], Kilany, R.[Rima],
Deep learning applications in pulmonary medical imaging: Recent updates and insights on COVID-19,
MVA(31), No. 6, August 2020, pp. Article53.
WWW Link. 2008

Afshar, P.[Parnian], Heidarian, S.[Shahin], Naderkhani, F.[Farnoosh], Oikonomou, A.[Anastasia], Plataniotis, K.N.[Konstantinos N.], Mohammadi, A.[Arash],
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images,
PRL(138), 2020, pp. 638-643.
Elsevier DOI 1806
COVID-19 pandemic, X-ray images, Deep learning, Capsule network BibRef

Wang, Z.[Zheng], Xiao, Y.[Ying], Li, Y.[Yong], Zhang, J.[Jie], Lu, F.G.[Fang-Gen], Hou, M.Z.[Mu-Zhou], Liu, X.W.[Xiao-Wei],
Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays,
PR(110), 2021, pp. 107613.
Elsevier DOI 2011
COVID-19, Computer-aided detection (CAD), Community-acquired pneumonia (CAP), Deep learning (DL), Chest X-ray (CXR) BibRef

Boulant, O.[Oliver], Fekom, M.[Mathilde], Pouchol, C.[Camille], Evgeniou, T.[Theodoros], Ovchinnikov, A.[Anton], Porcher, R.[Raphaël], Vayatis, N.[Nicolas],
SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic,
IPOL(10), 2020, pp. 150-166.
DOI Link 2011

Polsinelli, M.[Matteo], Cinque, L.[Luigi], Placidi, G.[Giuseppe],
A light CNN for detecting COVID-19 from CT scans of the chest,
PRL(140), 2020, pp. 95-100.
Elsevier DOI 2012
Deep Learning, CNN, Pattern Recognition, COVID-19 BibRef

Sebastianelli, A.[Alessandro], Mauro, F.[Francesco], di Cosmo, G.[Gianluca], Passarini, F.[Fabrizio], Carminati, M.[Marco], Ullo, S.L.[Silvia Liberata],
AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing,
IJGI(10), No. 1, 2021, pp. xx-yy.
DOI Link 2101

Dey, N.[Nilanjan], Zhang, Y.D.[Yu-Dong], Rajinikanth, V., Pugalenthi, R., Raja, N.S.M.[N. Sri Madhava],
Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays,
PRL(143), 2021, pp. 67-74.
Elsevier DOI 2102
Chest X-Ray, Pneumonia, VGG19 Architecture, Deep-Learning, Ensemble Feature Scheme BibRef

Akgundogdu, A.[Abdurrahim],
Detection of pneumonia in chest X-ray images by using 2D discrete wavelet feature extraction with random forest,
IJIST(31), No. 1, 2021, pp. 82-93.
DOI Link 2102
image classification, machine learning, pneumonia, random forest, wavelet BibRef

Feng, J.B.[Jun-Bang], Guo, Y.[Yi], Wang, S.[Shike], Shi, F.[Feng], Wei, Y.[Ying], He, Y.C.[Yi-Chu], Zeng, P.[Ping], Liu, J.[Jun], Wang, W.J.[Wen-Jing], Lin, L.P.[Li-Ping], Yang, Q.N.[Qing-Ning], Li, C.M.[Chuan-Ming], Liu, X.H.[Xing-Hua],
Differentiation between COVID-19 and bacterial pneumonia using radiomics of chest computed tomography and clinical features,
IJIST(31), No. 1, 2021, pp. 47-58.
DOI Link 2102
bacterial pneumonia, COVID-19, CT, LightGBM, radiomics BibRef

Öztürk, S.[Saban], Özkaya, U.[Umut], Barstugan, M.[Mücahid],
Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features,
IJIST(31), No. 1, 2021, pp. 5-15.
DOI Link 2102
classification, coronavirus, COVID-19, feature extraction, hand-crafted features, sAE BibRef

Zhou, T.X.[Tong-Xue], Canu, S.[Stéphane], Ruan, S.[Su],
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism,
IJIST(31), No. 1, 2021, pp. 16-27.
DOI Link 2102
attention mechanism, COVID-19, CT, deep learning, focal tversky loss, segmentation BibRef

Selvaraj, D.[Deepika], Venkatesan, A.[Arunachalam], Mahesh, V.G.V.[Vijayalakshmi G. V.], Raj, A.N.J.[Alex Noel Joseph],
An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images,
IJIST(31), No. 1, 2021, pp. 28-46.
DOI Link 2102
artificial intelligence, computed tomography image, deep neural network, feature extraction, Zernike moment BibRef

Chen, Y., 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.,
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation,
IP(30), 2021, pp. 3113-3126.
COVID-19, Computed tomography, Image segmentation, Sensitivity, X-rays, Pandemics, Lung, COVID-19, joint diagnosis, CT classification, COVID-19 dataset BibRef

Oulefki, A.[Adel], Agaian, S.[Sos], Trongtirakul, T.[Thaweesak], Kassah Laouar, A.[Azzeddine],
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images,
PR(114), 2021, pp. 107747.
Elsevier DOI 2103
Corona-virus Ddisease (COVID-19), Computer-Aaided Ddetection (CAD), COVID-19 lesion, 3D Visualization BibRef

Li, J.P.[Jin-Peng], Zhao, G.M.[Gang-Ming], Tao, Y.L.[Ya-Ling], Zhai, P.H.[Peng-Hua], Chen, H.[Hao], He, H.G.[Hui-Guang], Cai, T.[Ting],
Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19,
PR(114), 2021, pp. 107848.
Elsevier DOI 2103
Computed tomography, X-ray, COVID-19, Deep learning, Multi-task learning, Contrastive learning BibRef

Shorfuzzaman, M.[Mohammad], Hossain, M.S.[M. Shamim],
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients,
PR(113), 2021, pp. 107700.
Elsevier DOI 2103
COVID-19 diagnosis, Multi-shot learning, Contrastive loss, CXR images, Siamese network BibRef

Chen, X.C.[Xiao-Cong], Yao, L.[Lina], Zhou, T.[Tao], Dong, J.M.[Jin-Ming], Zhang, Y.[Yu],
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images,
PR(113), 2021, pp. 107826.
Elsevier DOI 2103
COVID-19 diagnosis, Few-shot learning, Contrastive learning, Chest CT images BibRef

He, K.[Kelei], Zhao, W.[Wei], Xie, X.Z.[Xing-Zhi], Ji, W.[Wen], Liu, M.X.[Ming-Xia], Tang, Z.Y.[Zhen-Yu], Shi, Y.H.[Ying-Huan], Shi, F.[Feng], Gao, Y.[Yang], Liu, J.[Jun], Zhang, J.F.[Jun-Feng], Shen, D.G.[Ding-Gang],
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images,
PR(113), 2021, pp. 107828.
Elsevier DOI 2103
COVID-19, CT, Severity assessment, Lung lobe segmentation, Multi-instance learning BibRef

Samulowska, M.[Marta], Chmielewski, S.[Szymon], Raczko, E.[Edwin], Lupa, M.[Michal], Myszkowska, D.[Dorota], Zagajewski, B.[Bogdan],
Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103

Upadhyay, K.[Kamini], Agrawal, M.[Monika], Deepak, D.[Desh],
Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images,
IET-IPR(14), No. 16, 19 December 2020, pp. 4059-4066.
DOI Link 2103

Tiwari, S.[Shamik], Jain, A.[Anurag],
Convolutional capsule network for COVID-19 detection using radiography images,
IJIST(31), No. 2, 2021, pp. 525-539.
DOI Link 2105
capsule network, convolutional neural network, COVID-19, decision support system, deep learning, visual geometry group, X-ray BibRef

Polat, H.[Hasan], Özerdem, M.S.[Mehmet Siraç], Ekici, F.[Faysal], Akpolat, V.[Veysi],
Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks,
IJIST(31), No. 2, 2021, pp. 509-524.
DOI Link 2105
classification, computer-aided diagnosis, convolutional neural networks, coronavirus, COVID-19, radiology BibRef

Khan, M.A.[Murtaza Ali],
An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning,
IJIST(31), No. 2, 2021, pp. 499-508.
DOI Link 2105
artificial intelligence, chest X-ray radiograph, COVID-19, feature descriptors, medical image processing BibRef

Dhaka, V.S.[Vijaypal Singh], Rani, G.[Geeta], Oza, M.G.[Meet Ganpatlal], Sharma, T.[Tarushi], Misra, A.[Ankit],
A deep learning model for mass screening of COVID-19,
IJIST(31), No. 2, 2021, pp. 483-498.
DOI Link 2105
CNN model, Corona, COVID-19, deep learning, global pandemic, X-ray BibRef

El-dosuky, M.A.[Mohamed A.], Soliman, M.[Mona], Hassanien, A.E.[Aboul Ella],
COVID-19 vs influenza viruses: A cockroach optimized deep neural network classification approach,
IJIST(31), No. 2, 2021, pp. 472-482.
DOI Link 2105
cockroach swarm optimization, convolutional neural networks, coronavirus, COVID-19, deep learning, influenza, SARS-CoV-2 BibRef

Dash, T.K.[Tusar Kanti], Mishra, S.[Soumya], Panda, G.[Ganapati], Satapathy, S.C.[Suresh Chandra],
Detection of COVID-19 from speech signal using bio-inspired based cepstral features,
PR(117), 2021, pp. 107999.
Elsevier DOI 2106
Bio-inspired computing, COVID19, Speech signal BibRef

Fan, Y.Q.[Yu-Qi], Liu, J.H.[Jia-Hao], Yao, R.X.[Rui-Xuan], Yuan, X.H.[Xiao-Hui],
COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network,
PR(119), 2021, pp. 108055.
Elsevier DOI 2106
Deep learning, Attention, Coronavirus, X-ray images, Multi-scale BibRef

de Carvalho Brito, V.[Vitória], dos Santos, P.R.S.[Patrick Ryan Sales], de Sales Carvalho, N.R.[Nonato Rodrigues], de Carvalho Filho, A.O.[Antonio Oseas],
COVID-index: A texture-based approach to classifying lung lesions based on CT images,
PR(119), 2021, pp. 108083.
Elsevier DOI 2106
COVID-19, Computed tomography, 3D texture analysis, Phylogenetic diversity BibRef

Irmak, E.[Emrah],
COVID-19 disease severity assessment using CNN model,
IET-IPR(15), No. 8, 2021, pp. 1814-1824.
DOI Link 2106

Koyuncu, H.[Hasan], Barstugan, M.[Mücahid],
COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier,
SP:IC(97), 2021, pp. 116359.
Elsevier DOI 2107
Binary categorization, Chaotic, Coronavirus, Framework design, Hybrid classifier, Optimization BibRef

Zhao, S.X.[Shi-Xuan], Li, Z.D.[Zhi-Dan], Chen, Y.[Yang], Zhao, W.[Wei], Xie, X.Z.[Xing-Zhi], Liu, J.[Jun], Zhao, D.[Di], Li, Y.J.[Yong-Jie],
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images,
PR(119), 2021, pp. 108109.
Elsevier DOI 2108
COVID-19, Convolutional neural network, Segmentation, Lung opacification, Attention mechanism BibRef

Shaban, W.M.[Warda M.], Rabie, A.H.[Asmaa H.], Saleh, A.I.[Ahmed I.], Abo-Elsoud, M.A.,
Accurate detection of COVID-19 patients based on distance biased Naďve Bayes (DBNB) classification strategy,
PR(119), 2021, pp. 108110.
Elsevier DOI 2108
COVID-19, Classification, NB, Feature selection, Wrapper, Optimization, Particle swarm BibRef

Hu, J.L.[Jin-Long], Xu, S.H.[Song-Hua], Ding, X.D.[Xiang-Dong],
A Triplet network framework based automatic assessment of simulation quality for respiratory droplet propagation,
PR(119), 2021, pp. 108060.
Elsevier DOI 2108
Simulation quality assessment, Respiratory droplet propagation, Triplet network, Attentive temporal pooling BibRef

Vieira, P.[Pablo], Sousa, O.[Orrana], Magalhăes, D.[Deborah], Rabęlo, R.[Ricardo], Silva, R.[Romuere],
Detecting pulmonary diseases using deep features in X-ray images,
PR(119), 2021, pp. 108081.
Elsevier DOI 2108
COVID-19, X-ray, Deep learning, Pre-processing BibRef

Zhao, C.[Chen], Xu, Y.[Yan], He, Z.[Zhuo], Tang, J.S.[Jin-Shan], Zhang, Y.J.[Yi-Jun], Han, J.G.[Jun-Gang], Shi, Y.X.[Yu-Xin], Zhou, W.H.[Wei-Hua],
Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images,
PR(119), 2021, pp. 108071.
Elsevier DOI 2108
COVID-19, Chest CT, Pulmonary parenchyma segmentation, Deep learning, 3D V-Net BibRef

Cao, W.[Wen], Dai, H.R.[Hao-Ran], Zhu, J.W.[Jing-Wen], Tian, Y.Z.[Yu-Zhen], Peng, F.L.[Fei-Lin],
Analysis and Evaluation of Non-Pharmaceutical Interventions on Prevention and Control of COVID-19: A Case Study of Wuhan City,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108

Cho, Y.[Yongwon], Hwang, S.H.[Sung Ho], Oh, Y.W.[Yu-Whan], Ham, B.J.[Byung-Joo], Kim, M.J.[Min Ju], Park, B.J.[Beom Jin],
Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets,
IJIST(31), No. 3, 2021, pp. 1087-1104.
DOI Link 2108
chest radiography, computer-aided diagnosis (CAD), COVID-19, deep learning, lung diseases BibRef

Johri, S.[Shikhar], Goyal, M.[Mehendi], Jain, S.[Sahil], Baranwal, M.[Manoj], Kumar, V.[Vinay], Upadhyay, R.[Rahul],
A novel machine learning-based analytical framework for automatic detection of COVID-19 using chest X-ray images,
IJIST(31), No. 3, 2021, pp. 1105-1119.
DOI Link 2108
chest X-ray images, coronavirus, machine learning methods, pneumonia BibRef

Tang, L.[Lu], Tian, C.[Chuangeng], Meng, Y.[Yankai], Xu, K.[Kai],
Longitudinal evaluation for COVID-19 chest CT disease progression based on Tchebichef moments,
IJIST(31), No. 3, 2021, pp. 1120-1127.
DOI Link 2108
blur, COVID-19 CT image, disease progression, objective evaluation, Tchebichef moments BibRef

Zhang, X.[XiaoQing], Wang, G.Y.[Guang-Yu], Zhao, S.G.[Shu-Guang],
COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation,
IJIST(31), No. 3, 2021, pp. 1071-1086.
DOI Link 2108
convolution neural network, COVID-19, image segmentation, lung CT image BibRef

Yu, F.[Fuli], Zhu, Y.[Yu], Qin, X.X.[Xiang-Xiang], Xin, Y.[Ying], Yang, D.W.[Da-Wei], Xu, T.[Tao],
A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images,
IET-IPR(15), No. 11, 2021, pp. 2604-2613.
DOI Link 2108

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.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.
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.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

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.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

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

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.
COVID-19, Task analysis, Lung, Imaging, Ultrasonic imaging, Semantics, Training, COVID-19, deep learning, image classification, semantic segmentation BibRef

Karthik, R., Menaka, R., Hariharan, M., Won, D.[Daehan],
Contour-enhanced attention CNN for CT-based COVID-19 segmentation,
PR(125), 2022, pp. 108538.
Elsevier DOI 2203
COVID-19, Segmentation, Deep learning, Attention, Decoder, CNN BibRef

Hu, H.G.[Hai-Gen], Shen, L.Z.[Lei-Zhao], Guan, Q.[Qiu], Li, X.X.[Xiao-Xin], Zhou, Q.W.[Qian-Wei], Ruan, S.[Su],
Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images,
PR(124), 2022, pp. 108452.
Elsevier DOI 2203
Semantic segmentation, Multi-scale features, Attention mechanism, Feature fusion, COVID-19 BibRef

Bao, G.Q.[Guo-Qing], Chen, H.[Huai], Liu, T.L.[Tong-Liang], Gong, G.Z.[Guan-Zhong], Yin, Y.[Yong], Wang, L.S.[Li-Sheng], Wang, X.[Xiuying],
COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment,
PR(124), 2022, pp. 108499.
Elsevier DOI 2203
COVID-19, Multitask learning, 3D CNNs, Diagnosis, Severity assessment, Deep learning, Computer tomography BibRef

Dentamaro, V.[Vincenzo], Giglio, P.[Paolo], Impedovo, D.[Donato], Moretti, L.[Luigi], Pirlo, G.[Giuseppe],
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath,
PR(127), 2022, pp. 108656.
Elsevier DOI 2205
Audio classification, Spectrograms, Attention mechanism, Covid, Pre-screening, Convolutional neural network BibRef

Rabie, A.H.[Asmaa H.], Mansour, N.A.[Nehal A.], Saleh, A.I.[Ahmed I.], Takieldeen, A.E.[Ali E.],
Expecting individuals' body reaction to Covid-19 based on statistical Naďve Bayes technique,
PR(128), 2022, pp. 108693.
Elsevier DOI 2205
Covid-19, Prediction, Naďve Bayes, Prudential Expectation BibRef

Li, F.[Fudong], Lu, X.Y.[Xing-Yu], Yuan, J.J.[Jian-Jun],
MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification,
MedImg(41), No. 5, May 2022, pp. 1208-1218.
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.
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

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

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.
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.
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

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 of chest x-ray,
IJIST(32), No. 5, 2022, pp. 1447-1463.
DOI Link 2209
automatic detection, COVID-19, deep feature extraction with a lightweight CNN, Relief, SVM BibRef

Kumar, S.[Sachin], Shastri, S.[Sourabh], Mahajan, S.[Shilpa], Singh, K.[Kuljeet], Gupta, S.[Surbhi], Rani, R.[Rajneesh], Mohan, N.[Neeraj], Mansotra, V.[Vibhakar],
LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images,
IJIST(32), No. 5, 2022, pp. 1464-1480.
DOI Link 2209
chest X-ray, classification, COVID-19, deep neural network, LiteCovidNet BibRef

Polat, H.[Hasan],
A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections,
IJIST(32), No. 5, 2022, pp. 1481-1495.
DOI Link 2209
computed tomography, COVID-19, deep learning, DeepLabV3 +, ResNet, segmentation BibRef

Chi, J.N.[Jian-Ning], Zhang, S.[Shuang], Han, X.Y.[Xiao-Ying], Wang, H.[Huan], Wu, C.D.[Cheng-Dong], Yu, X.S.[Xiao-Sheng],
MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images,
SP:IC(108), 2022, pp. 116835.
Elsevier DOI 2209
COVID-19, Infection segmentation, CT image, Deep learning, Convolutional neural networks BibRef

Sanchez, K.[Karen], Hinojosa, C.[Carlos], Arguello, H.[Henry], Kouamé, D.[Denis], Meyrignac, O.[Olivier], Basarab, A.[Adrian],
CX-DaGAN: Domain Adaptation for Pneumonia Diagnosis on a Small Chest X-Ray Dataset,
MedImg(41), No. 11, November 2022, pp. 3278-3288.
Pulmonary diseases, X-ray imaging, Medical diagnostic imaging, Deep learning, Training, X-rays, Lung, Chest X-ray, deep learning, pneumonia diagnosis BibRef

Saberian, M.S.[M. Sadegh], Moriarty, K.P.[Kathleen P.], Olmstead, A.D.[Andrea D.], Hallgrimson, C.[Christian], Jean, F.[François], Nabi, I.R.[Ivan R.], Libbrecht, M.W.[Maxwell W.], Hamarneh, G.[Ghassan],
DEEMD: Drug Efficacy Estimation Against SARS-CoV-2 Based on Cell Morphology With Deep Multiple Instance Learning,
MedImg(41), No. 11, November 2022, pp. 3128-3145.
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.
Computed tomography, COVID-19, Lung, Pulmonary diseases, Feature extraction, Predictive models, Sensitivity, lung computed tomography (CT) scans BibRef

Ma, L.[Lu], Song, S.[Shuni], Guo, L.T.[Li-Ting], Tan, W.J.[Wen-Jun], Xu, L.S.[Li-Sheng],
COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet,
IJIST(33), No. 1, 2023, pp. 6-17.
DOI Link 2301
area loss function, computed tomography (CT) image segmentation, COVID-19, pre-activated residual block BibRef

Sanghvi, H.A.[Harshal A.], Patel, R.H.[Riki H.], Agarwal, A.[Ankur], Gupta, S.[Shailesh], Sawhney, V.[Vivek], Pandya, A.S.[Abhijit S.],
A deep learning approach for classification of COVID and pneumonia using DenseNet-201,
IJIST(33), No. 1, 2023, pp. 18-38.
DOI Link 2301
bio-medical innovation, CNN classification, COVID detection, deep learning, medical imaging, X-ray imaging BibRef

Gupta, H.[Harsh], Bansal, N.[Naman], Garg, S.[Swati], Mallik, H.[Hritesh], Prabha, A.[Anju], Yadav, J.[Jyoti],
A hybrid convolutional neural network model to detect COVID-19 and pneumonia using chest X-ray images,
IJIST(33), No. 1, 2023, pp. 39-52.
DOI Link 2301
chest X-rays, CNN, COVID-19, hybrid model, pneumonia, transfer learning techniques BibRef

Acharya, U.K.[Upendra Kumar], Ali, M.T.[Mohammad Taha], Ahmed, M.K.[Mohd Kaif], Siddiqui, M.T.[Mohd Tabish], Gupta, H.[Harsh], Kumar, S.[Sandeep], Mishra, A.S.[Ajey Shakti],
Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images,
IJIST(33), No. 4, 2023, pp. 1129-1143.
DOI Link 2307
BM3D, CLAHE, Darknet, deep convolutional neural network, inception V3, ResNet50, transfer learning BibRef

Eyiokur, F.I.[Fevziye Irem], Kantarci, A.[Alperen], Erakin, M.E.[Mustafa Ekrem], Damer, N.[Naser], Ofli, F.[Ferda], Imran, M.[Muhammad], Križaj, J.[Janez], Salah, A.A.[Albert Ali], Waibel, A.[Alexander], Štruc, 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.B.[Yan-Bei], Li, H.[Henan], Luo, T.[Tao], Zhang, C.Q.[Chang-Qing], Xiao, Z.[Zhitao], Wei, Y.[Ying], Gao, Y.Z.[Yao-Zong], Shi, F.[Feng], Shan, F.[Fei], Shen, D.G.[Ding-Gang],
Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity,
MedImg(42), No. 2, February 2023, pp. 557-567.
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

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.
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.
Feature extraction, COVID-19, Task analysis, Meteorology, Probability distribution, Wind forecasting, Viruses (medical), target-specific MB variable BibRef

Kordnoori, S.[Shirin], Sabeti, M.[Malihe], Mostafaei, H.[Hamidreza], Banihashemi, S.S.A.[Saeed Seyed Agha],
Analysis of lung scan imaging using deep multi-task learning structure for Covid-19 disease,
IET-IPR(17), No. 5, 2023, pp. 1534-1545.
DOI Link 2304
image classification, image segmentation BibRef

Kordnoori, S.[Shirin], Sabeti, M.[Maliheh], Mostafaei, H.[Hamidreza], Banihashemi, S.S.A.[Saeed Seyed Agha],
An efficient deep multi-task learning structure for COVID-19 disease,
IET-IPR(17), No. 13, 2023, pp. 3728-3745.
DOI Link 2311
image enhancement, medical image processing BibRef

Han, Z.Y.[Zhong-Yi], Gui, X.J.[Xian-Jin], Sun, H.L.[Hao-Liang], Yin, Y.L.[Yi-Long], Li, S.[Shuo],
Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments,
PAMI(45), No. 5, May 2023, pp. 6460-6479.
Noise measurement, Task analysis, COVID-19, Training, Machine learning algorithms, X-ray imaging, Supervised learning, non-stationary environments BibRef

Jeevitha, S., Valarmathi, K.,
A joint segmentation and classification framework for COVID-19 infection segmentation and detection from chest CT images,
IJIST(33), No. 3, 2023, pp. 789-806.
DOI Link 2305
attention, COVID-19, CT images, densenet, multi-task learning, YNet BibRef

Patel, R.K.[Rajneesh Kumar], Kashyap, M.[Manish],
Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images,
IJIST(33), No. 3, 2023, pp. 807-821.
DOI Link 2305
COVID-19, CT image, Gabor filter, machine learning, VMD BibRef

Agrali, M.[Mahmut], Kilic, V.[Volkan], Onan, A.[Aytug], Koç, E.M.[Esra Meltem], Koç, A.M.[Ali Murat], Büyüktoka, R.E.[Rasit Eren], Acar, T.[Türker], Adibelli, Z.[Zehra],
DeepChestNet: Artificial intelligence approach for COVID-19 detection on computed tomography images,
IJIST(33), No. 3, 2023, pp. 776-788.
DOI Link 2305
artificial intelligence, computer-aided diagnosis system, COVID-19 detection, lung segmentation, pulmonary lobe segmentation BibRef

Zeng, L.L.[Ling-Li], Gao, K.[Kai], Hu, D.[Dewen], Feng, Z.C.[Zhi-Chao], Hou, C.P.[Chen-Ping], Rong, P.F.[Peng-Fei], Wang, W.[Wei],
SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation,
PAMI(45), No. 8, August 2023, pp. 10427-10442.
COVID-19, Lesions, Image segmentation, Lung, Computed tomography, Labeling, Training, COVID-19, CT image, deep learning, diagnosis, semi-supervised learning BibRef

Zhang, Y.D.[Yu-Dong],
Fighting against COVID-19: Innovations and applications,
IJIST(33), No. 4, 2023, pp. 1111-1115.
DOI Link 2307

Ibrahim, A.U.[Abdullahi Umar], Kibarer, A.G.[Ayse Gunnay], Al-Turjman, F.[Fadi], Kaba, S.[Serife],
Large-scaled detection of COVID-19 from X-ray using transfer learning,
IJIST(33), No. 4, 2023, pp. 1116-1128.
DOI Link 2307
AlexNet, COVID-19, CT-scan, deep learning, SARS-CoV-2, SVM, X-ray BibRef

Erdem, K.[Kenan], Kobat, M.A.[Mehmet Ali], Bilen, M.N.[Mehmet Nail], Balik, Y.[Yunus], Alkan, S.[Sevim], Cavlak, F.[Feyzanur], Poyraz, A.K.[Ahmet Kursad], Barua, P.D.[Prabal Datta], Tuncer, I.[Ilknur], Dogan, S.[Sengul], Baygin, M.[Mehmet], Erten, M.[Mehmet], Tuncer, T.[Turker], Tan, R.S.[Ru-San], Acharya, U.R.[U. Rajendra],
Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images,
IJIST(33), No. 4, 2023, pp. 1144-1159.
DOI Link 2307
AlexNet, biomedical image classification, CT image classification, Hybrid-Patch-Alex, transfer learning BibRef

Bagwan, F.[Faraz], Pise, N.[Nitin],
A precise and timely graph-based approach to identify SARS Covid19 infection from medical imaging data using IsoCovNet,
IJIST(33), No. 4, 2023, pp. 1160-1176.
DOI Link 2307
convolutional networks, CT-scan, graph isomorphism network (GIN), graph neural network (GNN), x-ray BibRef

Samantaray, L.[Leena], Panda, R.[Rutuparna], Naik, M.K.[Manoj Kumar], Abraham, A.[Ajith],
A novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 X-ray image analysis,
IJIST(33), No. 4, 2023, pp. 1177-1193.
DOI Link 2307
biomedical image processing, COVID-19 X-ray image analysis, multiclass segmentation, radiology BibRef

Das, D.[Dolly], Biswas, S.K.[Saroj Kumar], Bandyopadhyay, S.[Sivaji],
Mixed attention and regularized COVID-19 network: An approach to detection of COVID-19 with chest x-ray images,
IJIST(33), No. 4, 2023, pp. 1194-1222.
DOI Link 2307
channel feature extraction, COVID-19, deep convolutional neural network, mixed attention, spatial feature extraction BibRef

Ewaidat, H.A.[Haytham Al], Balawi, S.[Sara], Bataineh, Z.[Ziad], Al-Dwairi, A.[Ahmed], Al-Khalily, M.[Majd], Azez, K.A.[Khalaf Abdel], Almakhadmeh, A.[Ali],
Establishment of national diagnostic reference levels as guidelines for computed tomography radiation in Jordan,
IJIST(33), No. 4, 2023, pp. 1223-1234.
DOI Link 2307
CT scan, CTDIv, diagnostic reference levels BibRef

Yan, N.[Nan], Tao, Y.[Ye],
Pneumonia X-ray detection with anchor-free detection framework and data augmentation,
IJIST(33), No. 4, 2023, pp. 1235-1246.
DOI Link 2307
anchor-free detection framework, computer-aided diagnosis, data augmentation, pneumonia detection BibRef

Hertel, R.[Robert], Benlamri, R.[Rachid],
Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging,
Surveys(55), No. 12, March 2023, pp. xx-yy.
DOI Link 2307
neural networks, radiology, Machine learning, infectious disease BibRef

Naz, M.[Maria], Shah, M.A.[Munam Ali], Khattak, H.A.[Hasan Ali], Wahid, A.[Abdul], Asghar, M.N.[Muhammad Nabeel], Rauf, H.T.[Hafiz Tayyab], Khan, M.A.[Muhammad Attique], Ameer, Z.[Zoobia],
Multi-branch sustainable convolutional neural network for disease classification,
IJIST(33), No. 5, 2023, pp. 1621-1633.
DOI Link 2310
chest computerized tomography (CT) scan, disease classification, machine learning, corona virus disease-19 (COVID-19) BibRef

Sameer, H.A.[Humam Adnan], Mutlag, A.H.[Ammar Hussein], Gharghan, S.K.[Sadik Kamel],
Deep learning-based COVID-19 diagnosis using CT scans with laboratory and physiological parameters,
IET-IPR(17), No. 11, 2023, pp. 3127-3142.
DOI Link 2310
convolutional neural network, computed tomography scan, COVID-19, deep learning, diagnosis, physiological parameters BibRef

Lu, F.F.[Fang-Fang], Zhang, Z.H.[Zhi-Hao], Liu, T.X.[Tian-Xiang], Tang, C.[Chi], Bai, H.[Hualin], Zhai, G.T.[Guang-Tao], Chen, J.J.[Jing-Jing], Wu, X.X.[Xiao-Xin],
A weakly supervised inpainting-based learning method for lung CT image segmentation,
PR(144), 2023, pp. 109861.
Elsevier DOI 2310
COVID-19, Weakly supervised, Lesion segmentation, Image inpainting BibRef

Dong, A.[Aimei], Liu, J.[Jian], Zhang, G.D.[Guo-Dong], Wei, Z.[Zhonghe], Zhai, Y.[Yi], Lv, G.H.[Guo-Hua],
Momentum contrast transformer for COVID-19 diagnosis with knowledge distillation,
PR(143), 2023, pp. 109732.
Elsevier DOI 2310
Momentum contrastive learning, Knowledge distillation, Vision transformer, COVID-19 diagnosis BibRef

Varma, O.R.[Om Ramakisan], Kalra, M.[Mala], Kirmani, S.[Sheeraz],
COVID-19: A systematic review of prediction and classification techniques,
IJIST(33), No. 6, 2023, pp. 1829-1857.
DOI Link 2311
COVID-19 datasets, COVID-19 detection, COVID-19 manual diagnosis, deep learning, machine learning BibRef

Mhamdi, L.[Lotfi], Dammak, O.[Oussama], Cottin, F.[François], Ben Dhaou, I.[Imed],
Deep learning for COVID-19 contamination analysis and prediction using ECG images on Raspberry Pi 4,
IJIST(33), No. 6, 2023, pp. 1858-1869.
DOI Link 2311
COVID-19, deep learning, ECG images, embedded systems, healthcare, SARS-CoV-2 virus BibRef

Yang, Y.[Yuan], Zhang, L.[Lin], Ren, L.[Lei], Wang, X.H.[Xiao-Han],
Distributed autoencoder classifier network for small-scale and scattered COVID-19 dataset classification,
IJIST(33), No. 6, 2023, pp. 1870-1881.
DOI Link 2311
autoencoder, COVID-19 dataset, deep learning, small-scale BibRef

Salama, A.N.[Aya Nader], Mohamed, M.A., Amer, H.M.[Hanan M.], Ata, M.M.[Mohamed Maher],
An efficient quantification of COVID-19 in chest CT images with improved semantic segmentation using U-Net deep structure,
IJIST(33), No. 6, 2023, pp. 1882-1901.
DOI Link 2311
COVID-19 quantification, deep learning, image processing, semantic segmentation, U-Net BibRef

Perumal, M.[Murukessan], Srinivas, M.,
DenSplitnet: Classifier-invariant neural network method to detect COVID-19 in chest CT data,
JVCIR(97), 2023, pp. 103949.
Elsevier DOI 2312
Chest-CT-scan images, Novel coronavirus, COVID-19, Deep learning, Self-supervised learning, Computer vision BibRef

Shea, D.E.[Daniel E], Kulhare, S.[Sourabh], Millin, R.[Rachel], Laverriere, Z.[Zohreh], Mehanian, C.[Courosh], Delahunt, C.B.[Charles B], Banik, D.[Dipayan], Zheng, X.L.[Xin-Liang], Zhu, M.[Meihua], Ji, Y.[Ye], Ostbye, T.[Travis], Mehanian, M.M.S.[Martha-Marie S], Uwajeh, A.[Atinuke], Akinsete, A.M.[Adeseye M], Wang, F.[Fen], Horning, M.P.[Matthew P],
Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia,

Ali, H.[Hazrat], Grönlund, C.[Christer], Shah, Z.[Zubair],
Leveraging GANs for data scarcity of COVID-19: Beyond the hype,

Cores, D.[Daniel], Vila-Blanco, N.[Nicolás], Mucientes, M.[Manuel], Carreira, M.J.[María J.],
Few-shot Image Classification for Automatic Covid-19 Diagnosis,
Springer DOI 2307

Galán-Cuenca, A.[Alejandro], Mirón, M.[Miguel], Gallego, A.J.[Antonio Javier], Saval-Calvo, M.[Marcelo], Pertusa, A.[Antonio],
Inter vs. Intra Domain Study of Covid Chest X-ray Classification with Imbalanced Datasets,
Springer DOI 2307

Turnbull, R.[Robert],
Using a 3d Resnet for Detecting the Presence and Severity of Covid-19 from CT Scans,
Springer DOI 2304

Kollias, D.[Dimitrios], Arsenos, A.[Anastasios], Kollias, S.[Stefanos],
Ai-mia: Covid-19 Detection and Severity Analysis Through Medical Imaging,
Springer DOI 2304

Bougourzi, F.[Fares], Distante, C.[Cosimo], Dornaika, F.[Fadi], Taleb-Ahmed, A.[Abdelmalik],
CNR-IEMN-CD and CNR-IEMN-CSD Approaches for Covid-19 Detection and Covid-19 Severity Detection from 3d Ct-scans,
Springer DOI 2304

Berenguer, A.D.[Abel Díaz], Mukherjee, T.[Tanmoy], Da, Y.F.[Yi-Fei], Bossa, M.N.[Matías Nicolás], Kvasnytsia, M.[Maryna], Vandemeulebroucke, J.[Jef], Deligiannis, N.[Nikos], Sahli, H.[Hichem],
Representation Learning with Information Theory to Detect Covid-19 and Its Severity,
Springer DOI 2304

Hsu, C.C.[Chih-Chung], Tsai, C.H.[Chi-Han], Chen, G.L.[Guan-Lin], Ma, S.D.[Sin-Di], Tai, S.C.[Shen-Chieh],
Spatial-slice Feature Learning Using Visual Transformer and Essential Slices Selection Module for Covid-19 Detection of Ct Scans in the Wild,
Springer DOI 2304

Hou, J.L.[Jun-Lin], Xu, J.[Jilan], Zhang, N.[Nan], Wang, Y.[Yi], Zhang, Y.[Yuejie], Zhang, X.B.[Xiao-Bo], Feng, R.[Rui],
CMC_V2: Towards More Accurate Covid-19 Detection with Discriminative Video Priors,
Springer DOI 2304

Kienzle, D.[Daniel], Lorenz, J.[Julian], Schön, R.[Robin], Ludwig, K.[Katja], Lienhart, R.[Rainer],
Covid Detection and Severity Prediction with 3d-convnext and Custom Pretrainings,
Springer DOI 2304

Tan, W.J.[Wei-Jun], Yao, Q.[Qi], Liu, J.[Jingfeng],
Two-stage Covid19 Classification Using Bert Features,
Springer DOI 2304

Zheng, L.[Lilang], Fang, J.X.[Jia-Xuan], Tang, X.R.[Xiao-Run], Li, H.Z.[Han-Zhang], Fan, J.X.[Jia-Xin], Wang, T.Y.[Tian-Yi], Zhou, R.[Rui], Yan, Z.Y.[Zhao-Yan],
PVT-COV19D: Covid-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer,
Springer DOI 2304

Hou, J.L.[Jun-Lin], Xu, J.[Jilan], Zhang, N.[Nan], Zhang, Y.J.[Yue-Jie], Zhang, X.B.[Xiao-Bo], Feng, R.[Rui],
Boosting Covid-19 Severity Detection with Infection-aware Contrastive Mixup Classification,
Springer DOI 2304

Weninger, L.[Leon], Romanzetti, S.[Sandro], Ebert, J.[Julia], Reetz, K.[Kathrin], Merhof, D.[Dorit],
Harmonization of Diffusion MRI Data Obtained with Multiple Head Coils Using Hybrid Cnns,
Springer DOI 2304

Nakhli, R.[Ramin], Darbandsari, A.[Amirali], Farahani, H.[Hossein], Bashashati, A.[Ali],
CCRL: Contrastive Cell Representation Learning,
Springer DOI 2304

Zhang, Z.[Zhen], Guo, D.L.[Da-Lei],
Unsupervised Domain Adaptation Based Automatic COVID-19 CT Segmentation,
COVID-19, Deep learning, Image segmentation, Visualization, Statistical analysis, Computed tomography, Training data, COVID-19, unsupervised domain adaptation BibRef

Tyagi, M.[Mrinal], Roy, S.[Santanu], Bansal, V.[Vibhuti],
Custom Weighted Balanced Loss function for Covid 19 Detection from an Imbalanced CXR Dataset,
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,
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,
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,
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,
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,
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,
Springer DOI 2208

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,
Springer DOI 2208

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,
Springer DOI 2208

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,
Springer DOI 2208

Spatafora, M.A.N.[Maria Ausilia Napoli], Ortis, A.[Alessandro], Battiato, S.[Sebastiano],
Mixup Data Augmentation for COVID-19 Infection Percentage Estimation,
Springer DOI 2208

Chaudhary, S.[Suman], Yang, W.T.[Wan-Ting], Qiang, Y.[Yan],
Swin Transformer for COVID-19 Infection Percentage Estimation from CT-Scans,
Springer DOI 2208

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,
Springer DOI 2208

Zedda, L.[Luca], Loddo, A.[Andrea], di Ruberto, C.[Cecilia],
A Shallow Learning Investigation for COVID-19 Classification,
Springer DOI 2208

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,
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,
Springer DOI 2205

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,
Springer DOI 2205

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,
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,
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,
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.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,
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], 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,
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,
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.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,
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.Y.[Wei-Ya],
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.Y.[Dong-Yun], Li, Y.Q.[Yi-Qun], Sheng, D.[Dong], Min, O.Z.[Oo Zaw],
Rethinking of Deep Models Parameters with Respect to Data Distribution,
Training, Deep learning, COVID-19, Image segmentation, Annotations, Object detection, Data models BibRef

Xu, R.[Rui], Wang, Y.[Yi], Liu, T.T.[Tian-Tian], Ye, X.C.[Xin-Chen], Lin, L.[Lin], Chen, Y.W.[Yen-Wei], Kido, S.[Shoji], Tomiyama, N.[Noriyuki],
BG-Net: Boundary-Guided Network for Lung Segmentation on Clinical CT Images,
COVID-19, Deep learning, Image segmentation, Hospitals, Computed tomography, Pulmonary diseases, Lung, Lung Segmentation, CT Images BibRef

Xu, R.[Rui], Cao, X.[Xiao], Wang, Y.F.[Yu-Feng], Chen, Y.W.[Yen-Wei], Ye, X.C.[Xin-Chen], Lin, L.[Lin], Zhu, W.C.[Wen-Chao], Chen, C.[Chao], Xu, F.Y.[Fang-Yi], Zhou, Y.[Yong], Hu, H.J.[Hong-Jie], Kido, S.[Shoji], Tomiyama, N.[Noriyuki],
Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images,
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 .

Last update:Dec 8, 2023 at 20:54:15