Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

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 · see citing works

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 · see citing works

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 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

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 · see citing works

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 · see citing works

Untitled work

(2014) · Cited by 33,419 · see citing works

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 · see citing works

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 · see citing works

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges

Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., & Hsueh, P.-R. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105924 · Cited by 6,068 · see citing works

First case of 2019 novel coronavirus in the United States

Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., Spitters, C., Ericson, K., Wilkerson, S., Tural, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001191 · Cited by 6,030 · see citing works

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works

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 · see citing works

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

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 · see citing works

Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany

Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Jänke, C., Guggemos, W., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001468 · Cited by 4,143 · see citing works

Multi-column deep neural networks for image classification

Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · 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.2012.6248110 · Cited by 3,760 · see citing works

COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses

Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020) · Journal of Advanced Research · doi:10.1016/j.jare.2020.03.005 · Cited by 3,544 · see citing works

A Review of Coronavirus Disease-2019 (COVID-19)

Singhal, T. (2020) · The Indian Journal of Pediatrics · doi:10.1007/s12098-020-03263-6 · Cited by 3,542 · see citing works

A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation

Goutte, C., & Gaussier, E. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31865-1_25 · Cited by 2,285 · see citing works

Neural Networks: Tricks of the Trade: Second Edition

(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works

Review of Deep Learning Algorithms and Architectures

Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883 · cited 1× · see citing works

Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins

Lam, T. T., Jia, N., Zhang, Y.-W., Shum, M. H.-H., Jiang, J.-F., Zhu, H., Tong, Y., Shi, Y., Ni, X.-B., Liao, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2169-0 · Cited by 1,877 · see citing works

Learning methods for generic object recognition with invariance to pose and lighting

LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324 · see citing works

Deep learning: new computational modelling techniques for genomics

Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,229 · see citing works

The German Traffic Sign Recognition Benchmark: A multi-class classification competition

Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033395 · Cited by 1,190 · see citing works

Proceedings of the 24th International Conference on Neural Information Processing Systems

Shawe-Taylor, J. S., Zemel, R. S., Bartlett, P. L., Pereira, F. B., & Weinberger, K. Q. (2011) · Cited by 1,184 · see citing works

Importation and Human-to-Human Transmission of a Novel Coronavirus in Vietnam

Lan, P. T., Nguyễn, T. V., Lương, Q. C., Nguyen, T. V., Nguyen, H. T., Le, H. Q., Nguyen, T. T., Cao, T. M., & Pham, Q. D. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001272 · Cited by 1,139 · see citing works

Very deep convolutional neural network based image classification using small training sample size

Liu, S., & Deng, W. (2015) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2015.7486599 · Cited by 1,111 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012 · see citing works

SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far

Rabi, F. A., Zoubi, M. S. A., KASASBEH, G., Salameh, D. M., & Al‐Nasser, A. D. (2020) · Pathogens · doi:10.3390/pathogens9030231 · Cited by 773 · see citing works

A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., & de Albuquerque, V. H. C. (2020) · Applied Sciences · doi:10.3390/app10020559 · Cited by 753 · see citing works

COVID-19 Image Data CollectionPreprint

Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722 · see citing works

Coronavirus Disease 2019: Coronaviruses and Blood Safety

Chang, L., Yan, Y., & Wang, L. (2020) · Transfusion Medicine Reviews · doi:10.1016/j.tmrv.2020.02.003 · Cited by 655 · see citing works

An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Stephen, O., Sain, M., Maduh, U. J., & Jeong, D. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/4180949 · Cited by 621 · see citing works

A gentle introduction to deep learning in medical image processing

Maier, A., Syben, C., Lasser, T., & Rieß, C. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.12.003 · Cited by 519 · see citing works

Pneumonia Detection Using CNN based Feature Extraction

Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2019.8869364 · Cited by 463 · see citing works

Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning

Ayan, E., & Ünver, H. M. (2019) · Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) · doi:10.1109/ebbt.2019.8741582 · Cited by 440 · see citing works

Smart Augmentation Learning an Optimal Data Augmentation Strategy

Lemley, J., Bazrafkan, S., & Corcoran, P. (2017) · IEEE Access · doi:10.1109/access.2017.2696121 · Cited by 427 · see citing works

The first two cases of 2019‐nCoV in Italy: Where they come from?

Giovanetti, M., Benvenuto, D., Angeletti, S., & Ciccozzi, M. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25699 · Cited by 338 · see citing works

ICDAR 2013 Chinese Handwriting Recognition Competition

Yin, F., Wang, Q., Zhang, X.-Y., & Liu, C. (2013) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2013.218 · Cited by 324 · see citing works

The first 2019 novel coronavirus case in Nepal

Bastola, A., Sah, R., Rodriguez-Morales, A. J., Lal, B. K., Jha, R., Ojha, H. C., Shrestha, B., Chu, D. K. W., Poon, L. L. M., Costello, A. M. D. L., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30067-0 · Cited by 324 · see citing works

Diagnostic Testing for the Novel Coronavirus

Sharfstein, J. M., Becker, S. J., & Mello, M. M. (2020) · JAMA · doi:10.1001/jama.2020.3864 · Cited by 232 · see citing works

Deep Learning with Microfluidics for Biotechnology

Riordon, J., Sovilj, D., Sanner, S., Sinton, D. A., & Young, E. W. K. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.08.005 · Cited by 224 · see citing works

A Review: Generative Adversarial Networks

Gonog, L., & Zhou, Y. (2019) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2019.8833686 · Cited by 205 · see citing works

Multimedia Big Data Analytics

Pouyanfar, S., Yang, Y., Chen, S., Shyu, M., & Iyengar, S. S. (2018) · ACM Computing Surveys · doi:10.1145/3150226 · Cited by 195 · see citing works

Recent Advances of Generative Adversarial Networks in Computer Vision

Cao, Y., Jia, L., Chen, Y.-X., Lin, N., Yang, C., Zhang, B., Liu, Z., Li, X., & Dai, H. (2018) · IEEE Access · doi:10.1109/access.2018.2886814 · Cited by 191 · see citing works

Arabic Handwritten Characters Recognition Using Convolutional Neural Network

El-Sawy, A., Loey, M., & El-Bakry, H. M. (2017) · WSEAS Transactions on Computers archive · Cited by 159 · see citing works

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Khalifa, N. E. M., Loey, M., Taha, M. H. N., & Mohamed, H. (2019) · Acta Informatica Medica · doi:10.5455/aim.2019.27.327-332 · Cited by 148 · see citing works

Computer vision detection of foreign objects in walnuts using deep learning

Rong, D., Xie, L., & Ying, Y. B. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.05.019 · Cited by 139 · see citing works

Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach

Khalifa, N. E. M., Taha, M. H. N., Ezzat, D., Słowik, A., & Hassanien, A. E. (2020) · IEEE Access · doi:10.1109/access.2020.2970210 · Cited by 121 · see citing works

Game consumption and the 2019 novel coronavirus

Li, J., Li, J., Xie, X., Cai, X., Huang, J. V., Tian, X., & Zhu, H. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30063-3 · Cited by 92 · see citing works

Controllable Generative Adversarial Network

Lee, M., & Seok, J. (2019) · IEEE Access · doi:10.1109/access.2019.2899108 · Cited by 87 · see citing works

Aquarium Family Fish Species Identification System Using Deep Neural Networks

Khalifa, N. E. M., Taha, M. H. N., & Hassanien, A. E. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-99010-1_32 · Cited by 48 · see citing works

Novel coronavirus takes flight from bats?

York, A. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-0336-9 · Cited by 43 · see citing works

Deep GoogLeNet Features for Visual Object Tracking

Aswathy, P. B., Siddhartha, & Mishra, D. (2018) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2018.8721317 · Cited by 39 · see citing works

Deep Galaxy V2: Robust Deep Convolutional Neural Networks for Galaxy Morphology Classifications

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Selim, I. M. (2018) · International Conference on Computing Sciences and Engineering (ICCSE) · doi:10.1109/iccse1.2018.8374210 · Cited by 38 · see citing works

Automatic Detection of Pneumonia on Compressed Sensing Images using Deep Learning

Islam, S. R., Maity, S. P., Ray, A. K., & Mandal, M. K. (2019) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2019.8861969 · Cited by 37 · see citing works

Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Mohamed, H. (2019) · Acta Informatica Medica · doi:10.5455/aim.2019.27.96-102 · Cited by 34 · see citing works

Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

Caramihale, T., Popescu, D. M., & Ichim, L. (2018) · Symmetry · doi:10.3390/sym10090414 · Cited by 27 · see citing works

Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Hemedan, A. A. (2019) · International Journal of Reasoning-based Intelligent Systems · doi:10.1504/ijris.2019.102610 · Cited by 24 · see citing works

Arabic Handwritten Characters Recognition Using Convolutional Neural Network

Loey, M. (2020) · Figshare · doi:10.6084/m9.figshare.12236906 · Cited by 9 · see citing works

Federated learning for smart healthcare: A survey

Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827 · see citing works

Generative adversarial network: An overview of theory and applications

Aggarwal, A., Mittal, M., & Battineni, G. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100004 · Cited by 783 · see citing works

A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2020) · Measurement · doi:10.1016/j.measurement.2020.108288 · Cited by 752 · see citing works

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam, M. Z., Islam, M. M., & Asraf, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100412 · Cited by 691 · see citing works

A Decade Survey of Transfer Learning (2010–2020)

Niu, S., Liu, Y., Wang, J., & Song, H. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3054609 · Cited by 660 · see citing works

Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104037 · Cited by 615 · see citing works

Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102600 · Cited by 503 · see citing works

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases

Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104348 · Cited by 403 · see citing works

GAN-based anomaly detection: A review

Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., & Ding, N. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.093 · Cited by 398 · see citing works

A comprehensive survey of recent trends in deep learning for digital images augmentation

Khalifa, N. E. M., Loey, M., & Mirjalili, S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10066-4 · Cited by 404 · see citing works

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

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 · see citing works

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

Das, N. N., Kumar, N., Kaur, M., Kumar, V., & Singh, D. (2020) · IRBM · doi:10.1016/j.irbm.2020.07.001 · Cited by 351 · see citing works

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 354 · see citing works

Medical image analysis based on deep learning approach

Puttagunta, M. K., & Ravi, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10707-4 · Cited by 334 · see citing works

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., Qi, S., Kong, F., Zhu, X., & Zhao, X. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200715 · Cited by 302 · see citing works

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Islam, M. M., Karray, F. O., Alhajj, R., & Zeng, J. (2021) · IEEE Access · doi:10.1109/access.2021.3058537 · Cited by 292 · see citing works

A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images

Loey, M., Manogaran, G., & Khalifa, N. E. M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05437-x · Cited by 291 · see citing works

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286 · see citing works

ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

Ikechukwu, A. V., Murali, S., Deepu, R., & Shivamurthy, R. C. (2021) · Global Transitions Proceedings · doi:10.1016/j.gltp.2021.08.027 · Cited by 244 · see citing works

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis

Wang‎, S., Nayak, D. R., Guttery, D. S., Zhang, X., & Zhang, Y. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.11.005 · Cited by 239 · see citing works

Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect

Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117362 · Cited by 250 · see citing works

Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works

Applications of artificial intelligence in battling against covid-19: A literature review

Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik, A., Iliyasu, A. M., El-Rahiem, B. A., Samea, M. E. A., Abdel-Raheem, A., Hammad, M. A., Peng, J., El-Samie, F. E. A., & El‐Latif, A. A. A. (2020) · Viruses · doi:10.3390/v12070769 · Cited by 203 · see citing works

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Hasan, N., Bao, Y., Shawon, A., & Huang, Y. (2021) · SN Computer Science · doi:10.1007/s42979-021-00782-7 · Cited by 200 · see citing works

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

Alam, N.-A.-A., Ahsan, M., Based, M. A., Haider, J., & Kowalski, M. (2021) · Sensors · doi:10.3390/s21041480 · Cited by 185 · see citing works

Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision

Elharrouss, O., Akbari, Y., Almaadeed, N., & Al-Maadeed, S. A. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100645 · Cited by 207 · see citing works

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168 · see citing works

Review on COVID ‐19 diagnosis models based on machine learning and deep learning approaches

Alyasseri, Z. A. A., Al‐Betar, M. A., Doush, I. A., Awadallah, M. A., Abasi, A. K., Makhadmeh, S. N., Alomari, O. A., Abdulkareem, K. H., Adam, A., Damaševičius, R., et al. (2021) · Expert Systems · doi:10.1111/exsy.12759 · Cited by 169 · see citing works

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Alafif, T., Tehame, A. M., Bajaba, S. M., Barnawi, A., & Zia, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031117 · Cited by 162 · see citing works

EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers

Saha, P., Sadi, M. S., & Islam, M. M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100505 · Cited by 158 · see citing works

Lightweight deep learning models for detecting COVID-19 from chest X-ray images

Karakanis, S., & Leontidis, G. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104181 · Cited by 147 · see citing works

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A ReviewPreprint

Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Sadeghi, D., Moridian, P., Khadem, A. A., Alizadehsani, R., Hussain, S., Zare, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10785 · Cited by 149 · see citing works

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

Ghaderzadeh, M., & Asadi, F. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6677314 · Cited by 137 · see citing works

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2021) · International Journal of Biological Sciences · doi:10.7150/ijbs.58855 · Cited by 139 · see citing works

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Rajpal, S., Lakhyani, N., Singh, A., Kohli, R., & Kumar, N., V (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110749 · Cited by 134 · see citing works

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Malik, H., Anees, T., din, M.-Z., & Naeem, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13843-7 · Cited by 141 · see citing works

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

Gianchandani, N., Jaiswal, A., Singh, D., Kumar, V., & Kaur, M. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02669-6 · Cited by 123 · see citing works

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Rasheed, J., Jamıl, A., Hameed, A. A., Aftab, U., Aftab, J., Shah, S. A., & Draheim, D. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110337 · Cited by 121 · see citing works

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 135 · see citing works

Audio deepfakes: A survey

Khanjani, Z., Watson, G., & Janeja, V. P. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2022.1001063 · Cited by 121 · see citing works

Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays

Tsiknakis, N., Trivizakis, E., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Tsatsakis, A. M., Sánchez‑García, J., López‐González, R., Papanikolaou, N. K., Karantanas, A. H., et al. (2020) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2020.8797 · Cited by 116 · see citing works

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

Zhang, Y., Zhang, Z., Zhang, X., & Wang, S. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.06.021 · Cited by 113 · see citing works

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data

Loey, M., El–Sappagh, S., & Mirjalili, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105213 · Cited by 107 · see citing works

Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach

Arias-Londoño, J. D., Gómez-García, J. A., Moro-Velázquez, L., & Godino-Llorente, J. I. (2020) · IEEE Access · doi:10.1109/access.2020.3044858 · Cited by 104 · see citing works

Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

Chen, J. I. (2021) · Journal of ISMAC · doi:10.36548/jismac.2021.2.006 · Cited by 104 · see citing works

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Bhosale, Y. H., & Patnaik, K. S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104445 · Cited by 104 · see citing works

Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

Mohammad‐Rahimi, H., Nadimi, M., Ghalyanchi‐Langeroudi, A., Taheri, M., & Ghafouri‐Fard, S. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.638011 · Cited by 101 · see citing works

Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images

Ayan, E., Karabulut, B., & Ünver, H. M. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06127-z · Cited by 107 · see citing works

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 297 · see citing works

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

Barshooi, A. H., & Amirkhani, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103326 · Cited by 100 · see citing works

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3062771 · Cited by 99 · see citing works

An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

Taha, B. A., Mashhadany, Y. I. M. A., Bakar, A. A. A., Zan, M. S. D., & Arsad, N. (2020) · Sensors · doi:10.3390/s20236764 · Cited by 97 · see citing works

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Chamseddine, E., Mansouri, N., Soui, M., & Abed, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109588 · Cited by 92 · see citing works

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

Kaur, M., Kumar, V., Yadav, V., Singh, D., Kumar, N., & Das, N. N. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/8829829 · Cited by 88 · see citing works

Transformer‐optimized generation, detection, and tracking network for images with drainage pipeline defects

Ma, D., Fang, H., Wang, N., Lü, H., Matthews, J. C., & Zhang, C. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12970 · Cited by 88 · see citing works

A systematic review on AI/ML approaches against COVID-19 outbreak

Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87 · see citing works

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Khan, M. A., Alhaisoni, M. M., Tariq, U., Hussain, N., Majid, A., Damaševičius, R., & Maskeliūnas, R. (2021) · Sensors · doi:10.3390/s21217286 · Cited by 85 · see citing works

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

John, C. C., Ponnusamy, V., Sriharipriya, K. C., & Nandakumar, R. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3069213 · Cited by 83 · see citing works

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Akram, T., Attique, M., Gul, S., Shahzad, A., Altaf, M. W., Naqvi, S. S. R., Damaševičius, R., & Maskeliūnas, R. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00950-0 · Cited by 85 · see citing works

Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020

Wang, L., Wang, H., Huang, Y., Yan, B., Chang, Z., Liu, Z., Zhao, M., Cui, L., Song, J., & Li, F. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.110069 · Cited by 82 · see citing works

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat, H. J., Sakr, G. E., & Kilany, R. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01101-5 · Cited by 81 · see citing works

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

Aradhya, V. N. M., Mahmud, M., Guru, D. S., Agarwal, B., & Kaiser, M. S. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09774-w · Cited by 81 · see citing works

Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

Vidal, P. L., de Moura, J., Novo, J., & Ortega, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114677 · Cited by 74 · see citing works

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 · see citing works

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

Goel, T., Murugan, R., Mirjalili, S., & Chakrabartty, D. K. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09785-7 · Cited by 76 · see citing works

Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images

Malhotra, A., Mittal, S., Majumdar, P., Chhabra, S., Thakral, K., Mayank Vatsa, A., Singh, R., Chaudhury, S., Pudrod, A., & Agrawal, A. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108243 · Cited by 73 · see citing works

Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer LearningPreprint

Al-Rakhami, M. S., Islam, M. M., Islam, M. Z., Asraf, A., Sodhro, A. H., & Ding, W. (2020) · medRxiv · doi:10.1101/2020.08.24.20181339 · Cited by 72 · see citing works

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network

Quan, H., Xu, X., Zheng, T., Li, Z., Zhao, M., & Cui, X. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104399 · Cited by 71 · see citing works

Covid-19 Imaging Tools: How Big Data is Big?

Santosh, K., & Ghosh, S. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01747-2 · Cited by 67 · see citing works

Role of Artificial Intelligence in COVID-19 Detection

Gudigar, A., Raghavendra, U., Nayak, S., Ooi, C. P., Chan, W. Y., Gangavarapu, M. R., Dharmik, C., Samanth, J., Kadri, N. A., Hasikin, K. B., et al. (2021) · Sensors · doi:10.3390/s21238045 · Cited by 67 · see citing works

Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

Hashmi, K. A., Liwicki, M., Stricker, D., Afzal, M. A., Afzal, M. A., & Afzal, M. Z. (2021) · IEEE Access · doi:10.1109/access.2021.3087865 · Cited by 66 · see citing works

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Ching, S. L. W., Chuah, J. H., Tee, C. A. T., Anis, S., Shoaib, M. A., Amir, F., Khalil, A., & Lai, K. W. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5528144 · Cited by 66 · see citing works

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

Alam, F., Almaghthawi, A., Katib, I. A., Albeshri, A. A., & Mehmood, R. (2021) · Sustainability · doi:10.3390/su13073797 · Cited by 67 · see citing works

Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

Meraihi, Y., Gabis, A. B., Mirjalili, S., Ramdane-Chérif, A., & Alsaadi, F. E. (2022) · SN Computer Science · doi:10.1007/s42979-022-01184-z · Cited by 63 · see citing works

COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images

Srinivas, K., Sri, R. G., Pravallika, K., Nishitha, K., & Polamurı, S. R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15903-y · Cited by 63 · see citing works

Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review

Mondal, M. R. H., Bharati, S., & Podder, P. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210713113439 · Cited by 62 · see citing works

Deep learning-based COVID-19 detection system using pulmonary CT scans

Nair, R., Alhudhaif, A., Koundal, D., Doewes, R. I., & Sharma, P. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2105-243 · Cited by 62 · see citing works

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection

Gopatoti, A., & Vijayalakshmi, P. (2022) · Journal of X-Ray Science and Technology · doi:10.3233/xst-211113 · Cited by 62 · see citing works

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Rasheed, J., Jamıl, A., Hameed, A. A., Al-Turjman, F. M., & Rasheed, A. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00431-w · Cited by 61 · see citing works

A preliminary analysis of AI based smartphone application for diagnosis of COVID-19 using chest X-ray images

Rangarajan, A. K., & Ramachandran, H. K. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115401 · Cited by 61 · see citing works

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Hasan, M. M., Islam, M. U., Sadeq, M. J., Fung, W.-K., & Uddin, J. (2023) · Sensors · doi:10.3390/s23010527 · Cited by 68 · see citing works

Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons

Rehman, A. U., Saba, T., Tariq, U., & Ayesha, N. (2021) · IT Professional · doi:10.1109/mitp.2020.3036820 · Cited by 58 · see citing works

Generative Adversarial Networks: A Survey on Attack and Defense Perspective

Zhang, C., Yu, S., Tian, Z., & Yu, J. J. Q. (2023) · ACM Computing Surveys · doi:10.1145/3615336 · Cited by 62 · see citing works

COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models

Loey, M., & Mirjalili, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105020 · Cited by 56 · see citing works

An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

Jalali, S. M. J., Ahmadian, M., Ahmadian, S., Khosravi, A., Alazab, M., & Nahavandi, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107675 · Cited by 55 · see citing works

Current Advances in Paper-Based Biosensor Technologies for Rapid COVID-19 Diagnosis

Kim, S., & Lee, J. (2022) · BioChip Journal · doi:10.1007/s13206-022-00078-9 · Cited by 55 · see citing works

COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

Zulkifley, M. A., Abdani, S. R., & Zulkifley, N. H. (2020) · Symmetry · doi:10.3390/sym12091530 · Cited by 54 · see citing works

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh, R. A., Nilashi, M., Ismail, M. Y., Alhargan, A., Alghamdi, A., Alzahrani, A. O., Saraireh, L., Osman, R., & Asadi, S. (2021) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2021.11.013 · Cited by 53 · see citing works

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans

Bhatele, K. R., Jha, A., Tiwari, D., Bhatele, M., Sharma, S., Mithora, M. R., & Singhal, S. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10076-6 · Cited by 57 · see citing works

COVID-19 Detection in CT/X-ray Imagery Using Vision Transformers

Rahhal, M. M. A., Bazi, Y., Jomaa, R. M., AlShibli, A., Alajlan, N., Mekhalfi, M. L., & Melgani, F. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020310 · Cited by 52 · see citing works

Intelligent system for COVID-19 prognosis: a state-of-the-art survey

Nayak, J., Naik, B., Dinesh, P., Vakula, K., Rao, B. K., Ding, W., & Pelusi, D. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02102-7 · Cited by 51 · see citing works

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

Fan, X., Jiang, M., & Yan, H. (2021) · IEEE Access · doi:10.1109/access.2021.3095191 · Cited by 51 · see citing works

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient

Kumar, R., Arora, R., Bansal, V., Sahayasheela, V. J., Buckchash, H., Imran, J., Narayanan, N., Pandian, G. N., & Raman, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12500-3 · Cited by 51 · see citing works

A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Haque, K. F., & Abdelgawad, A. (2020) · AI · doi:10.3390/ai1030027 · Cited by 51 · see citing works

Classification of Positive COVID-19 CT Scans using Deep Learning

Khan, M. A., Hussain, N., Majid, A., Alhaisoni, M. M., Bukhari, S. A. C., Kadry, S., Nam, Y., & Zhang, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013191 · Cited by 50 · see citing works

BDCNet: multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs

Malik, H., Anees, T., & Mui-zzud-din (2022) · Multimedia Systems · doi:10.1007/s00530-021-00878-3 · Cited by 50 · see citing works

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images

Muhammad, U., Hoque, M. Z., Oussalah, M., Keskinarkaus, A., Seppänen, T., & Sarder, P. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108207 · Cited by 48 · see citing works

PD-ResNet for Classification of Parkinson’s Disease From Gait

Yang, X., Ye, Q., Cai, G., Wang, Y.-Q., & Cai, G. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3180933 · Cited by 49 · see citing works

A hybrid approach for COVID-19 detection: Combining Wasserstein GAN with transfer learning

Rounaq, S., Shah, S. M., Aljawarneh, M. M., & Muhammad, G. (2026) · Next research. · doi:10.1016/j.nexres.2026.102355 · Cited by 2 · see citing works

Detection of Coronavirus (COVID-19) Associated Pneumonia Based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model Using Chest X-ray Dataset

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Elghamrawy, S. M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_22 · Cited by 151 · see citing works

Detection of Covid-19 Cases Using Gan (Generative Adversarial Network)Preprint

Rounaq, S., Shaikh, G. M., & Siddiqui, D. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4427075

Covid-19 detection and overcome the scarcity of chest X-Ray datasets based on transfer learning and GAN model

Qadir, J. A., Jameel, S. K., & Majidpour, J. (2021) · International Conference on Communication & Information Technology (ICICT) · doi:10.1109/icict52195.2021.9568468 · Cited by 3 · see citing works

The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach

Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Taha, S. H. N. (2020) · Studies in big data · doi:10.1007/978-3-030-55258-9_5 · Cited by 15 · see citing works