The DOI matches its Crossref record; 130 of the 141 references it cites were found in a registry or an index, and 1 are not checked yet.
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Open accessAbstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Nevertheless, the ‘black-box’ nature [168], dependency on large labeled datasets, and high computational cost hinder their widespread clinical implementation.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [168]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_1 · Cited by 11,109
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Chan, J., Yuan, S.-F., Kok, K.-H., To, K., Chu, H., Yang, J., Xing, F., Liu, J.-L., Yip, C., Poon, R., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30154-9 · Cited by 8,450
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
2008 IEEE Conference on Computer Vision and Pattern Recognition
(2008) · doi:10.1109/cvpr12618.2008 · Cited by 4,263
2013 IEEE International Conference on Computer Vision
(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,232
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639
Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020) · Radiology · doi:10.1148/radiol.2020200432 · Cited by 3,340
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,652
Apostolopoulos, I. D., & Mpesiana, T. A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00865-4 · Cited by 2,338
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
2007 IEEE Conference on Computer Vision and Pattern Recognition
(2007) · doi:10.1109/cvpr12085.2007 · Cited by 1,726
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,694
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
Khan, A. I., Shah, J. L., & Bhat, M. M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105581 · Cited by 1,351
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,273
The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic
Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J., Martin, I. B. K., et al. (2020) · CHEST Journal · doi:10.1016/j.chest.2020.04.003 · Cited by 1,262
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,253
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint
Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,218
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01829-7 · Cited by 1,174
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
(2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9 · Cited by 1,149
Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J.-J., Martin, I. B. K., et al. (2020) · Radiology · doi:10.1148/radiol.2020201365 · Cited by 1,101
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Mei, X., Lee, H.-C., Diao, K.-Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0931-3 · Cited by 1,084
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 978
COVID-CT-Dataset: A CT Scan Dataset about COVID-19Preprint
Yang, X., Zhao, J.-Y., Zhang, Y.-C., He, X.-H., & Xie, P.-T. (2020) · arXiv.org · doi:10.48550/arxiv.2003.13865 · Cited by 926
Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.
(2014) · PubMed Central · doi:10.3978/j.issn.2223-4292.2014.11.20 · Cited by 918
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917 · cited 1×
“org [108], MosMedData [102], CNCB 2019 novel coronavirus resource (2019nCoVR) AI diagnosis dataset [128], COVID-19 CT lung and infection segmentation dataset [129], LIDC-IDRI [101], and integrative CT Images and Clinical Features for COVID-19 (iCTCF) [130].”
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Oh, Y., Park, S. J., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 912
Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103795 · Cited by 864
2016 Fourth International Conference on 3D Vision (3DV)
(2016) · doi:10.1109/3dv40526.2016 · Cited by 856
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · Medical Physics · doi:10.1002/mp.14609 · Cited by 802
Uçar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 768
Deep learning approaches for COVID-19 detection based on chest X-ray images
Ismael, A. M., & Şengür, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114054 · Cited by 754
Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100412 · Cited by 729
COVID-19 Image Data CollectionPreprint
Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images
Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.010 · Cited by 718
Rahimzadeh, M., & Attar, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100360 · Cited by 695
PadChest: A large chest x-ray image dataset with multi-label annotated reports
Bustos, A., Pertusa, A., Salinas, J.-M., & de la Iglesia-Vayá, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101797 · Cited by 691
2015 IEEE Winter Conference on Applications of Computer Vision
(2015) · doi:10.1109/wacv33479.2015 · Cited by 672
Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet
Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menéndez, R., & Singh, V. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109944 · Cited by 645
AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A. J., Janizek, J. D., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00338-7 · Cited by 631
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis
Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18685-1 · Cited by 611
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103805 · Cited by 574
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system
Wang, B., Jin, S., Yan, Q., Xu, H., Luo, C. W., Wei, L., Zhao, W. Z., Hou, X., Ma, W., Xu, Z., et al. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106897 · Cited by 548
Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Azad, R., Asadi-Aghbolaghi, M., Fathy, M., & Escalera, S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00052 · Cited by 534
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
Pereira, R. M., Bertolini, D. S., Teixeira, L., Junior, C. N. S., & Costa, Y. M. G. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105532 · Cited by 527
Mahmud, T., Rahman, M. A., & Fattah, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103869 · Cited by 525
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
Wu, Y.-H., Gao, S.-H., Mei, J., Xu, J., Fan, D.-P., Zhang, R.-G., & Cheng, M.-M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058783 · Cited by 516
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Bhattacharya, S., Maddikunta, P. K. R., Pham, Q., Gadekallu, T. R., Siva Rama Krishnan, S., Chowdhary, C. L., Alazab, M., & Piran, M. J. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102589 · Cited by 495
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., He, Y., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00775-2020 · Cited by 493
CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images
Hussain, E., Hasan, M., Rahman, A., Lee, I., Tamanna, T., & Parvez, M. Z. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110495 · Cited by 474
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Supatto, W. (2020) · doi:10.1109/isbi45749.2020 · Cited by 472
Deep Transfer Learning Based Classification Model for COVID-19 Disease
Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., Singh, S., & Shukla, P. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.05.003 · Cited by 462
Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104348 · Cited by 423
Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng, B. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104284 · Cited by 423
Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102365 · Cited by 401
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995508 · Cited by 379
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images
Tabik, S., Gómez-Ríos, A., Rodríguez, J. L. M., Sevillano-García, I., Rey-Area, M., Charte, D., Guirado, E., Suárez, J. L., Luengo, J., González, M. Á. V., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3037127 · Cited by 376
Luz, E. J. D. S., Silva, P., Silva, R., Silva, L., Guimarães, J., Miozzo, G., Moreira, G., & Menotti, D. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00151-6 · Cited by 370
Attention-based VGG-16 model for COVID-19 chest X-ray image classification
Sitaula, C., & Hossain, M. B. (2020) · Applied Intelligence · doi:10.1007/s10489-020-02055-x · Cited by 364
Wang, S., Govindaraj, V., Górriz, J. M., Zhang, X., & Zhang, Y. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.004 · Cited by 353
A deep learning approach to detect Covid-19 coronavirus with X-Ray images
Jain, G., Mittal, D., Thakur, D., & Mittal, M. K. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.08.008 · Cited by 335
A light CNN for detecting COVID-19 from CT scans of the chest
Polsinelli, M., Cinque, L., & Placidi, G. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.10.001 · Cited by 328
Toraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327
Diagnosis of COVID-19 using CT scan images and deep learning techniques
Shah, V., Keniya, R., Shridharani, A., Punjabi, M., Shah, J., & Mehendale, N. D. (2021) · Emergency Radiology · doi:10.1007/s10140-020-01886-y · Cited by 320
Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00343-w · Cited by 300
BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patientsPreprint
MdlI, V., Saborit-Torres, J. M., Ja, M., Pertusa, A., Bustos, A., Cazorla, M., Galant, J., Barber, X., Orozco‐Beltrán, D., García-García, F., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01174 · Cited by 288
The ensemble deep learning model for novel COVID-19 on CT images
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., & Dong, Y. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106885 · Cited by 255
Gupta, A., Anjum, Gupta, S., & Katarya, R. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106859 · Cited by 243
Self-citationDas, A. K., Ghosh, S., Thunder, S., Dutta, R., Agarwal, S., & Chakrabarti, A. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00970-4 · Cited by 241
Ning, W., Lei, S., Yang, J., Cao, Y., Jiang, P., Yang, Q., Zhang, J., Wang, X., Chen, F., Geng, Z., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00633-5 · Cited by 239
COVID-19 CT Lung and Infection Segmentation Dataset
Ma, J., Ge, C., Wang, Y., et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3757476 · Cited by 237
Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda‐Carreras, I., Collard, D., & Scherpereel, A. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01745-4 · Cited by 232
Ezzat, D., Hassanien, A. E., & Aboul-Ella, H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106742 · Cited by 226
COVID-19 CT Lung and Infection Segmentation Dataset
Ma, J., Cheng, G., Wang, Y., An, X., Gao, J., Yu, Z., Zhang, M., Liu, X., Xueyuan, D., Cao, S., et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3757475 · Cited by 225
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
Alshazly, H. A., Linse, C., Barth, E., & Martinetz, T. (2021) · Sensors · doi:10.3390/s21020455 · Cited by 222
Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays
Mukherjee, H., Ghosh, S., Dhar, A., Obaidullah, S. M., Santosh, K., & Roy, K. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01943-6 · Cited by 209
Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images
El‐kenawy, E. M., Ibrahim, A. F., Mirjalili, S., Eid, M. M., & Hussein, S. E. (2020) · IEEE Access · doi:10.1109/access.2020.3028012 · Cited by 199
Wang, Z., Xiao, Y., Li, Y., Zhang, J., Lu, F., Hou, M., & Liu, X. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107613 · Cited by 190
Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?
Pham, T. D. (2020) · Health Information Science and Systems · doi:10.1007/s13755-020-00135-3 · Cited by 190
Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier
Abraham, B., & Nair, M. S. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.08.005 · Cited by 169
SARS-CoV-2 CT-scan dataset:A large dataset of real patients CT scans for SARS-CoV-2 identification
Soares, E., Angelov, P. P., Biaso, S., Froes, M. H., & Abe, D. K. (2020) · Lancaster EPrints (Lancaster University) · Cited by 169
DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach
Sakib, S., Tazrin, T., Fouda, M. M., Fadlullah, Z. M., & Guizani, M. (2020) · IEEE Access · doi:10.1109/access.2020.3025010 · Cited by 165
Deep learning for diagnosis of COVID-19 using 3D CT scans
Serte, S., & Demirel, H. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104306 · Cited by 164
Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation
Kaul, C., Manandhar, S., & Pears, N. E. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759477 · Cited by 156
Varela-Santos, S., & Melín, P. (2020) · Information Sciences · doi:10.1016/j.ins.2020.09.041 · Cited by 154
Abdel‐Basset, M., Chang, V. I. C., Hawash, H., Chakrabortty, R. K., & Ryan, M. J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106647 · Cited by 131
Javaheri, T., Homayounfar, M., Amoozgar, Z., Reiazi, R., Homayounieh, F., Abbas, E., Laali, A., Radmard, A. R., Gharib, M. H., Mousavi, S. A., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00399-3 · Cited by 129
Thakur, S., & Kumar, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102920 · Cited by 123
Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN
Karthik, R., Menaka, R., & Hariharan, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106744 · Cited by 118
Robust chest CT image segmentation of COVID-19 lung infection based on limited data
Müller, D., Soto‐Rey, I., & Krämer, F. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100681 · Cited by 113
COVID-19 in diabetic patients: Related risks and specifics of management
Orioli, L., Hermans, M. P., Thissen, J.-P., Maiter, D. M., Vandeleene, B., & Yombi, J. C. (2020) · Annales d Endocrinologie · doi:10.1016/j.ando.2020.05.001 · Cited by 112
Irfan, M., Iftikhar, M. A., Yasin, S., Draz, U., Ali, T. M., Hussain, S., Bukhari, S., Alwadie, A. S., Rahman, S., Głowacz, A., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063056 · Cited by 102
Dhiman, G., Chang, V. I. C., Singh, K. K., & Shankar, A. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1875049 · Cited by 90
Social mimic optimization algorithm and engineering applications
Balochian, S., & Baloochian, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.035 · Cited by 89
Lin, Z. Q., Shafiee, M. J., Bochkarev, S., Jules, M. S., Wang, X. Y., & Wong, A. K. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07387 · Cited by 87
Türkoğlu, M. (2021) · IRBM · doi:10.1016/j.irbm.2021.01.004 · Cited by 85
Madaan, V., Roy, A., Gupta, C., Agrawal, P., Sharma, A., Bologa, C., & Prodan, R. (2021) · New Generation Computing · doi:10.1007/s00354-021-00121-7 · Cited by 80
Automated detection of COVID-19 from CT scan using convolutional neural network
Mishra, N. K., Singh, P., & Joshi, S. D. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.04.006 · Cited by 79
Self-citationFocusCovid: automated COVID-19 detection using deep learning with chest X-ray images
Agrawal, T., & Choudhary, P. (2021) · Evolving Systems · doi:10.1007/s12530-021-09385-2 · Cited by 79
A Comprehensive Survey of COVID-19 Detection Using Medical Images
Shah, F. M., Joy, S. K. S., Ahmed, F., Hossain, T., Humaira, M., Ami, A. S., Paul, S., Jim, M. A. R. K., & Ahmed, S. (2021) · SN Computer Science · doi:10.1007/s42979-021-00823-1 · Cited by 77
Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan
Arora, V., Ng, E. Y. K., Leekha, R. S., Darshan, M., & Singh, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104575 · Cited by 75
COV19-CNNet and COV19-ResNet: Diagnostic Inference Engines for Early Detection of COVID-19
Keleş, A., Keles, M. B., & Keleş, A. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09795-5 · Cited by 73
A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images
Kamil, M. Y. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i1.pp844-850 · Cited by 59
Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
Khadidos, A. O., Khadidos, A. O., Kannan, S., Natarajan, Y., Mohanty, S. N., & Tsaramirsis, G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.599550 · Cited by 55
Hariri, W., & Narin, A. (2021) · Soft Computing · doi:10.1007/s00500-021-06137-x · Cited by 44
Role of deep learning in early detection of COVID-19: Scoping review
Alzubaidi, M. S., Zubaydi, H. D., Bin-Salem, A. A., Abd-Alrazaq, A. A., Ahmed, A., & Househ, M. S. (2021) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2021.100025 · Cited by 41
Transfer-to-Transfer Learning Approach for Computer Aided Detection of COVID-19 in Chest Radiographs
Narayanan, B. N., Hardie, R. C., Krishnaraja, V., Karam, C., & Davuluru, V. S. P. (2020) · AI · doi:10.3390/ai1040032 · Cited by 40
Deepika, S., Venkatesan, A., Mahesh, V. G. V., & Raj, A. N. J. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22525 · Cited by 27
CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images
Al-Bawi, A., Al-Kaabi, K., Jeryo, M., & Al-Fatlawi, A. (2020) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00110-7 · Cited by 18
Research works printed but not linked: 2
- Hussain, 2021, vol. 142, 110495
- Shan, 2021, Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction, Med. Phys., 48, 1633, 10.1002/mp.14609 · DOI
Cited by
Sorted by citations
Ajagbe, S. A., & Adigun, M. O. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15805-z · Cited by 141
Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey
Siddiqi, R., & Javaid, S. (2024) · Journal of Imaging · doi:10.3390/jimaging10080176 · Cited by 145
Automated detection and forecasting of COVID-19 using deep learning techniques: A review
Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Sadeghi, D., Moridian, P., Khadem, A. A., Alizadehsani, R., Hussain, S., Zare, A., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127317 · Cited by 306
Kumar, S., Kumar, H., Kumar, G., Singh, S. P., Bijalwan, A., & Diwakar, M. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01192-w · Cited by 100
Ullah, N., Khan, J. A., El–Sappagh, S., El-Rashidy, N. M., & Khan, M. S. (2023) · Diagnostics · doi:10.3390/diagnostics13010162 · Cited by 73
Metadata
- Authors
- Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D.
- Affiliations
- Indian Institute of Technology Delhi (IN)
- CMR University (IN)
- National Institute of Technology Hamirpur (BD)
- Indraprastha Institute of Information Technology Delhi (IN)
- Corresponding author
- Singh, Pushpendra
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 144
- Pages
- 105350
- ISSN
- 0010-4825 (ISSN-L)
- PMC
- PMC8890789
- OpenAlex
- W4214860167
- Cited by
- 168
- References
- 141
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 43 · 2023: 46 · 2022: 23
- FWCI
- 6.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 pandemic, SARS-CoV-2, chest radiography, computed tomography, image classification, convolutional neural networks, automated diagnosis, medical image analysis, deep learning
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Artificial Intelligence, Humans, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review