Cited by 538COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and EducationAdvanced Neural Network Applications

The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Open access

Loey, Mohamed, Smarandache, Florentin, M. Khalifa, Nour Eldeen·2020·Symmetry

DOI 10.3390/sym12040651

Open this paper in CoChat — free account Open PDF

Abstract

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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

Crossref · 10.3390/sym12040651

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

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.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
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
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
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
Untitled work
(2014) · Cited by 33,419
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
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
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

Flagged first, then by citations

Show all 62 references

Research works printed but not linked: 3

Pouyanfar, 2018, A Survey on Deep Learning: Algorithms, Techniques, and Applications, ACM Comput. Surv., 51, 1
Hassanien, A.E., Shaalan, K., Gaber, T., Azar, A.T., and Tolba, M.F. (2016, January 24–26). CNN for Handwritten Arabic Digits Recognition Based on LeNet-5 BT. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, Cairo, Egypt.
Caruana, R., Lawrence, S., and Giles, L. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping. Proceedings of the 13th International Conference on Neural Information Processing Systems.

Sorted by citations

Items 301–400 of 537

Evaluation of deep learning for COVID‐19 diagnosis: Impact of image dataset organization
Kim, G. Y., Kim, J. Y., Kim, C. H., & Kim, S. M. (2021) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13320 · Cited by 7
Challenges and Future Directions for Segmentation of Medical Images Using Deep Learning Models
Birjais, R. (2025) · Wiley eBooks · doi:10.1002/9781394245369.ch10 · Cited by 7
DETECTION OF COVID FROM CHEST X-RAYS USING GAN
M, U. M., Lakshman, G. H., KhwairakpamBidyanandaSingh, & Pande, S. D. (2022) · EPRA International Journal of Research & Development (IJRD) · doi:10.36713/epra10226 · Cited by 6
Deep learning models/techniques for COVID-19 detection: a survey
Archana, K., Kaur, A., Gulzar, Y., Hamid, Y., Mir, M. S., & Soomro, A. B. (2023) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2023.1303714 · Cited by 6
DSENetk: An Efficient Deep Stacking Ensemble Approach for COVID-19 Induced Pneumonia Prediction Using Radiograph Images
Chaurasia, S., Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, A. K., Tiwari, P. P., Smirti, A., & Jangid, M. (2025) · SN Computer Science · doi:10.1007/s42979-024-03603-9 · Cited by 6
A Convolutional Neural Network (CNN) Based Framework for Enhanced Diagnosis and Classification of COVID-19 Pneumonia
Suliman, M., Malik, F., Khan, M. Q., Ullah, A., Rahman, N., & Shah, S. K. (2024) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v12i2.1999 · Cited by 7
Deep Residual Convolutional Neural Network Based Detection of Covid-19 from Chest-X-Ray Images
Rohini, V., Sobhana, M., Ch, S. C., Chinta, M., & Venna, D. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-73882-2_85 · Cited by 6
Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches
Messaoud, S., Bouaafia, S., Maraoui, A., Khriji, L., Ammari, A. C., & Machhout, M. (2022) · Canadian Journal of Infectious Diseases and Medical Microbiology · doi:10.1155/2022/6786203 · Cited by 6
Analysis of Chest X‐Ray Images for the Recognition of COVID‐19 Symptoms Using CNN
Hatamleh, W. A., Tarazi, H., Subbalakshmi, C., & Tiwari, B. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/3237361 · Cited by 6
Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data
Shukla, A. K., Seth, T., & Muhuri, P. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14642-4 · Cited by 8
The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images
Ikechukwu, A. V. (2024) · INTELIGENCIA ARTIFICIAL · doi:10.4114/intartif.vol27iss74pp62-79 · Cited by 6
Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach
Rounaq, S., Shah, S. M., Aljawarneh, M. M., Khan, S., & Muhammad, G. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42711 · Cited by 6
CNN Based Transfer Learning Framework For Classification Of COVID-19 Disease From Chest X-ray
Chanda, P. B., Banerjee, S., Dalai, V., & Ray, R. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432181 · Cited by 6
Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images
Karar, M. E., Shouman, M. A., & Chalopin, C. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018564 · Cited by 6
Intelligent Data Analysis for Infection Spread Prediction
Borovkov, A. I., Bolsunovskaya, M. V., & Gintciak, A. M. (2022) · Sustainability · doi:10.3390/su14041995 · Cited by 6
Deep Learning Approaches for Automated Diagnosis of COVID-19 Using Imbalanced Training CXR Data
Sharma, A., & Mishra, P. K. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-96040-7_36 · Cited by 6
A novel abnormality annotation database for COVID-19 affected frontal lung X-rays
Mittal, S., Venugopal, V. K., Agarwal, V., Malhotra, M., Chatha, J. S., Kapur, S., Gupta, A., Batra, V., Majumdar, P., Malhotra, A., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271931 · Cited by 6
COVID-19 Chest X-Ray Classification with Augmented GAN
Pranto, M. A. M., Asad, N. A., Palash, M. I. A., Islam, A. K. M. M., & Kaiser, M. S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2445-3_9 · Cited by 6
Federated Learning for COVID-19 Detection With Generative Adversarial Networks in Edge Cloud Computing
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., & Zomaya, A. Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3120998 · Cited by 5
Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
Amuda, K. A., WAKILI, A. L. M. U. S. T. A. P. H. A. A., Amoo, T., Agbetu, L., Wang, Q., & Feng, J. H. (2025) · Algorithms · doi:10.3390/a18070413 · Cited by 5
Explainable AI hybrid CNN-transformer models for enhanced COVID-19 detection in chest X-rays
Aravinda, C. V., Arjun, B. C., Khan, M. S., John, S., & Hussain, S. A. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00596-0 · Cited by 5
COVID-19 Chest X-rays Classification Through the Fusion of Deep Transfer Learning and Machine Learning Methods
Khalifa, N. E. M., Taha, M. H. N., Chakrabortty, R. K., & Loey, M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2948-9_1 · Cited by 5
Self-citation
COVID-19 classification in X-ray/CT images using pretrained deep learning schemes
Appavu, N., Babu, C. N. K., & Kadry, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18721-y · Cited by 5
Unsupervised generative learning-based decision-making system for COVID-19 detection
Menon, N., Yadav, P., Ravi, V., Acharya, V., & Sowmya, V. (2024) · Health and Technology · doi:10.1007/s12553-024-00879-y · Cited by 5
The Role of Emerging Technologies for Combating COVID-19 Pandemic
El-Aziz, A. A. A., Khalifa, N. E. M., Darwsih, A., & Hassanien, A. E. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-63307-3_2 · Cited by 5
Face Mask Detection in the Era of COVID-19: A CNN-Based Approach
Rezoana, N., Hossain, M. S., & Andersson, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7597-3_1 · Cited by 5
COVID-19 Cases Detection from Chest X-Ray Images using CNN based Deep Learning Model
Islam, M. A., Stea, G., Mahmud, S. A. S., & Rahman, K. M. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.01305108 · Cited by 5
Recent Trends of Federated Learning for Smart Healthcare Systems
Handa, T., Singhal, I., Chakraborty, P., & Kaur, G. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1082-3.ch005 · Cited by 5
Diagnostic Accuracy of Sagittal TSE-T2W, Variable Flip Angle 3D TSET2W and High-resolution 3D Heavily T2W Sequences for the Stenosis of Two Localizations: The Cerebral Aqueduct and the Superior Medullary Velum
Özcan, A. N. Ş., & Aslan, K. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210806123720 · Cited by 5
A Deep Learning Based Covid-19 Detection Framework
Jibril, F., Adeshina, S. A., & Nnanna, A. A. (2021) · International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) · doi:10.1109/icmeas52683.2021.9739806 · Cited by 5
A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING
Yaşar, H., & Ceylan, M. (2022) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.vol35no4.5 · Cited by 5
Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach
BabaAhmadi, A., Khalafi, S., Shariat-Panahi, M., & Ayati, M. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00167-4 · Cited by 5
IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
Kumar, R. G., Toomula, S., Paulraj, D., Jebin Bose, S., Bikku, T., & Sivabalaselvamani, D. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85854-0.00008-3 · Cited by 5
Automatic Classification of COVID-19 from Chest X-Ray Image using Convolutional Neural Network
Islam, T., Islam, R., Basak, M., & Roy, A. D. (2021) · International Conference on Electrical Information and Communication Technology (EICT) · doi:10.1109/eict54103.2021.9733477 · Cited by 5
Efficiency of Artificial Intelligence in Detecting COVID-19 Pneumonia and Other Pneumonia Causes by Quantum Fourier Transform MethodPreprint
Acar, E., Öztoprak, B., Reşorlu, M., Daş, M., Yılmaz, İ., & Öztoprak, İ. (2021) · medRxiv · doi:10.1101/2020.12.29.20248900 · Cited by 7
Deep Learning Based COVID-19 Detection: Challenges and Future DirectionsPreprint
Khan, M. K., Arshad, Q., Azam, F., & Khan, W. Z. (2021) · doi:10.36227/techrxiv.14625885 · Cited by 5
SecureFed: Federated learning empowered medical imaging technique to detect COVID-19 using chest x-raysPreprint
Makkar, A., & Santosh, K. (2022) · Research Square · doi:10.21203/rs.3.rs-1943509/v1 · Cited by 5
GANCHEST: a multi-GAN-based framework for chest CXR image generation and validation
Al-Shargabi, A. A., Alshobaili, J. F., Alabdulatif, A., Alrobah, N. A., & Ibrahim, D. M. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00227-8 · Cited by 4
A Semi-Supervised Domain Adaptation Approach for Diagnosing SARS-CoV-2 and its Variants of Concern (VOC)
Khattar, A., & Ouadri, S. M. K. (2021) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito51393.2021.9596381 · Cited by 4
Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging
Moghaddam, M. J., & Ghavipour, M. (2022) · IPEM-Translation · doi:10.1016/j.ipemt.2022.100008 · Cited by 5
Image Enhancement using ESRGAN for CNN based X-Ray Classification
Sharma, P., Mohanram, B., Vijayashree, J., Jayashree, J., & Sahoo, A. R. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072472 · Cited by 4
Artificial Intelligence Based Transfer Learning Approach in Identifying and Detecting Covid-19 Virus from CT-Scan Images
Sivakumar, S., Haritha, D., Rajalakshmi, R., Shanmugan, S., & Nagaraj, J. (2023) · Springer eBooks · doi:10.1007/978-3-031-30101-8_9 · Cited by 4
Schematized study for tackling COVID-19 with Machine Learning (ML), Artificial Intelligence (AI), and Internet of Things (IoT)
Sheth, V., Priyal, A., Mehta, K., Desai, N., & Shah, M. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.04.003 · Cited by 5
Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database
Talib, M. W. A., Moufti, M. A., Nasir, Q., Kabbani, Y., Aljaghber, D., & Afadar, Y. (2024) · International Dental Journal · doi:10.1016/j.identj.2024.08.002 · Cited by 4
Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images
Alotaibi, A. M. (2020) · International Journal of Machine Learning and Networked Collaborative Engineering · doi:10.30991/ijmlnce.2020v04i01.003 · Cited by 4
Evolution of conditional-GANs for the synthesis of chest x-ray images
de la Cruz, J. A. R., Mesa, H. G. A., Mezura‐Montes, E., Cosı́o, F. A., Ramírez, B. E., & Olveres, J. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606272 · Cited by 4
A Lightweight COVID-19 predictive model with Synthetic CT images using Conditional GAN & Knowledge Distillation
Jain, S., Mittal, S., & Bhat, A. (2022) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat53423.2022.9726022 · Cited by 4
Covid-19 Detection based on Transfer Learning & LSTM Network using X-ray Images
Ahirwar, R. K., Rasool, A., Chouhan, A., Dubey, A., Mehra, S., & Kumar, A. (2022) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic55036.2022.9848852 · Cited by 4
Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation
Li, Z., Li, Z., Yao, L., Chen, Q., Zhang, J., Li, X. S., Feng, J., Li, Y., & Xu, J. (2022) · JMIR Bioinformatics and Biotechnology · doi:10.2196/36660 · Cited by 4
Covariate-Shift Generative Adversarial Network and Railway Track Image Analysis
Balogun, I., & Attoh-Okine, N. (2022) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-7390 · Cited by 4
Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection
Nawaz, S., Rasheed, S., Sami, W., Hussain, L., Aldweesh, A. Y., Eldin, E. T., Salaria, U. A., & Khan, M. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037543 · Cited by 5
COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning
Ahmad, I. S., Li, N., Wang, T., Liu, X., Dai, J., Chan, Y., Liu, H., Zhu, J., Kong, W., Lu, Z., et al. (2023) · Bioengineering · doi:10.3390/bioengineering10111314 · Cited by 4
Computer-Aided Detection (CAD) for COVID-19 based on Chest X-Ray Images using Convolutional Neural Network
Pratiwi, N. C., Ibrahim, N., Fuadah, Y. N., & Masykuroh, K. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/982/1/012004 · Cited by 4
Deep Learning for COVID-19 Prognosis: A Systematic Review
Swapnarekha, H., Behera, H. S., Nayak, J., & Naik, B. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8439-8_53 · Cited by 4
The Impact of Histogram Equalization and Color Mapping on ResNet-34's Overall Performance for COVID-19 Detection
Freire, J. D., Montenegro, J., Mejia, H., Guzman, F. P., Bustamante, C. E., Velasteguí, R., & Guachi‐Guachi, L. (2021) · Data Storage and Data Engineering · doi:10.1145/3456146.3456154 · Cited by 4
Deep Learning Empowered Fight Against COVID-19: A Survey
Chowdhary, C. L., & Channi, H. K. (2022) · Studies in computational intelligence · doi:10.1007/978-981-19-2416-3_14 · Cited by 4
Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators
Shi, J., Du, J., Ren, Y., Li, B., Zou, J., & Zhang, A. (2022) · Journal of Sensors · doi:10.1155/2022/7832117 · Cited by 5
Retracted
Artificial Intelligence and the Control of COVID-19: A Review of Machine and Deep Learning Approaches
Folorunso, S. O., Ogbuju, E., & Oladipo, F. O. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-69744-0_10 · Cited by 4
A novel study for automatic two-class COVID-19 diagnosis (between COVID-19 and Healthy, Pneumonia) on X-ray images using texture analysis and 2-D/3-D convolutional neural networks
Yaşar, H., & Ceylan, M. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00892-z · Cited by 4
Detection of COVID-19 & non-COVID diseases from chest-X-rays using deep learning-based CNNs
Deepak, G. D., & Bhat, S. K. (2025) · Soft Computing · doi:10.1007/s00500-025-10905-4 · Cited by 3
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
COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology
Nayan, N. A., Yi, C. J., Suboh, M. Z., Mazlan, N.-F., Periyasamy, P. R., Rahim, M. Y. Z. A., & Shah, S. A. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.920849 · Cited by 3
CAD: Computer-Aided Detection of Pneumonia Using Convolutional Neural Networks (CNN)
Jain, M., Shah, A., Sharma, P. R., & Campisi, M. (2025) · International Journal of Computer Science and Mobile Computing · doi:10.47760/ijcsmc.2025.v14i05.005 · Cited by 4
COVID-19 Detection on CT Scans Using Local Binary Pattern and Deep Learning
Serte, S., & Al-Turjman, F. M. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-76063-2_7 · Cited by 3
PCN2: Parallel CNN to Diagnose COVID-19 from Radiographs and Metadata
Baz, A., & Baz, M. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.020304 · Cited by 3
A Survey on COVID-19 Detection from X-ray Images using Deep Learning
Sachin, S., & Bhat, A. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580164 · Cited by 3
Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)
Awan, M. J., Imtiaz, M. W., Usama, M., Rehman, A. U., Ayesha, N., & Shehzad, H. M. F. (2021) · International Conference on Innovative Computing (ICIC) · doi:10.1109/icic53490.2021.9693071 · Cited by 3
Delaunay triangulation based intelligent system for the diagnosis of covid from the low radiation CXR images
Sasikaladevi, N. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04329-3 · Cited by 3
Stacked Generative Adversarial Networks for Image Generation based on U-Net discriminator
Feng, W., Meng, Z., & Wang, L. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00132 · Cited by 3
Classification of Viral, Bacterial, And Covid-19 Pneumonia using Deep Learning Framework from Chest X-Ray Images
Chowdhury, M. E. H., Rahman, T., Khandakar, A. A., & Mahmud, S. (2022) · doi:10.5121/csit.2022.122001 · Cited by 3
Prompt Learning for Multi-modal COVID-19 Diagnosis
Yu, Y., Rong, L., Wang, M., Huang, M., Zhang, Y., & Ding, Y. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995157 · Cited by 3
Deep Learning Model for COVID-19 Classification Using Fine Tuned ResNet50 on Chest X-Ray Images
Dokun, O., John-Otumu, A. M., Eze, U. F., Ikerionwu, C., Etus, C., Nwanga, E., & Okonkwo, O. (2024) · Machine Learning Research · doi:10.11648/j.mlr.20240901.12 · Cited by 3
Detection of Covid‐19 disease by using privacy‐aware artificial intelligence system
İSMETOĞLU, A., & Canbay, Y. (2024) · Security and Privacy · doi:10.1002/spy2.434 · Cited by 3
AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance
Hanafi, M. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8070077 · Cited by 5
An Improved Yolov10n for Detection of Bronchoalveolar Lavage Cells
Jiang, P., Li, S., Lu, Y., & Song, X. (2025) · IEEE Access · doi:10.1109/access.2025.3532493 · Cited by 3
COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models
Nahar, K. M. O., Megdad, M. E., Mamlook, R. E. A., Aljaidi, M., Nahar, B. K. M., Nahar, M. K. M., Anbarkhan, S. H., Khafajeh, H., & Amer, W. (2024) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit62805.2024.10877037 · Cited by 3
Unsupervised Convolutional Filter Learning for COVID-19 Classification
Mahalingam, S. G., & Pandraju, S. (2021) · Revue d intelligence artificielle · doi:10.18280/ria.350509 · Cited by 3
An Investigation on Transfer Learning for Classification of COVID-19 Chest X-Ray Images with Pre-trained Convolutional-based Architectures
Nemati, M. A., & BabaAhmadi, A. (2022) · doi:10.1109/icee55646.2022.9827361 · Cited by 3
CNN-based Transfer Learning for Covid-19 Diagnosis
Ahmed, Z. S., Surameery, N. M. S., Rashid, R. D., Salih, S. Q., & Abdulla, H. K. (2021) · doi:10.1109/icit52682.2021.9491126 · Cited by 3
A cognitive framework based on deep neural network for classification of coronavirus disease
Kumari, S., & Bhatia, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03756-6 · Cited by 3
Transfer Learning Method with Deep Residual Network for COVID-19 Diagnosis Using Chest Radiographs Images
Komal, A., & Malik, H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7618-5_13 · Cited by 3
Chest Radiographs Images Retrieval Using Deep Learning Networks
Mahmoud, S. M., Al-Jubouri, H., & Abdulabbas, T. E. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i3.3478 · Cited by 3
A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection
Khan, H. U., Khan, S., & Nazir, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.875971 · Cited by 3
An Efficient Approach for Automatic detection of COVID-19 using Transfer Learning from Chest X-Ray Images
Priyatharshini, R., Aswath, R. A. S., Sreenidhi, M., Joshi, S. S., & Dhandapani, R. (2021) · doi:10.1109/icspc51351.2021.9451819 · Cited by 3
Generative Adversarial Networks
Pujahari, R. M., Khan, R., & Yadav, S. P. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91660-1_1 · Cited by 3
Deep Learning Fusion for COVID-19 DiagnosisPreprint
Kechagias‐Stamatis, O., Aouf, N., & Koukos, J. A. (2020) · medRxiv · doi:10.1101/2020.12.11.20246546 · Cited by 3
CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray ImagesPreprint
Khan, M. S. I., Rahman, A., Karim, M. R., Bithi, N. I., Band, S. S., Dehzangi, A., & Alinejad-Rokny, H. (2021) · medRxiv · doi:10.1101/2021.05.19.21257430 · Cited by 3
A Multi-Task Pipeline with Specialized Streams for Classification and Segmentation of Infection Manifestations in COVID-19 ScansPreprint
Al-Kabbany, A., El-Bana, S., & Sharkas, M. (2020) · doi:10.36227/techrxiv.12560168 · Cited by 3
Diagnosis of Covid 19 disease using CT scan images and pre-trained modelsPreprint
Amouzegar, F., Mirvaziri, H., Ahsaee, M. G., & Shariatzadeh, M. (2022) · Research Square · doi:10.21203/rs.3.rs-2356313/v1 · Cited by 3
A Weighted-Average-Ensembling Based Hybrid CNN Model for Improved Covid-19 DetectionPreprint
Patel, S., & Khan, N. R. (2023) · Research Square · doi:10.21203/rs.3.rs-3238828/v1 · Cited by 3
Data Augmentation using Generative Adversarial Network with Enhanced Deep Convolutional Neural Network for Pneumonia Chest X-ray ImagesPreprint
Porkodi, S. P., & Sarada, V. (2024) · Research Square · doi:10.21203/rs.3.rs-3336289/v1 · Cited by 3
A review of intelligent medical imaging diagnosis for the COVID-19 infection
Saurabh, N., & Shetty, J. (2022) · Intelligent Decision Technologies · doi:10.3233/idt-200178 · Cited by 2
Feasibility Study on Browser-Based Federated Machine Learning (FML) Architecture for Medical Application
Yang, L. T., Wei, H. W., & Xi, C. C. (2022) · International Conference on Green Energy, Computing and Sustainable Technology (GECOST) · doi:10.1109/gecost55694.2022.10010643 · Cited by 2
RAPID AUTOMATIC DETECTION OF COVID-19 IN CHEST CT IMAGES USING VGG-16 AND TRANSFER LEARNING
Gomroki, M., Shah–Hosseini, R., & Hasanlou, M. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-4-w2-2022-39-2023 · Cited by 2
Suspicious Permissions Detection of Covid-19 Themed Malicious Android Applications
Sharma, M., & Kaul, A. (2023) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic57335.2023.10044167 · Cited by 2
Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method
Ngong, I. C., & Baykan, N. A. (2023) · Traitement du signal · doi:10.18280/ts.400101 · Cited by 2
CNN Models Using Chest X-Ray Images for COVID-19 Detection: A Survey
Mellal, N., & Zaidi, S. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370621 · Cited by 2
Lung Disease Detection Based on Deep Learning Techniques: A Review
Katke, A., Kulkarni, K., Dharmadhikari, R., Rajput, A., Munot, M. V., & Jaiswal, R. C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-1320-2_26 · Cited by 2
Trapezoidal neutrosophic teaching learning-based optimization in enhancing accuracy of diabetes prognosis
Nivedita, Agrawal, S., Kumar, T., Kumar, K., Sharma, M. K., & Mishra, V. N. (2024) · Results in Control and Optimization · doi:10.1016/j.rico.2024.100464 · Cited by 2
Covid-19 Chest X-Ray Classification using Convolutional Neural Network
Moqbel, M. A. A. A., Radzi, M. D. I. M., Halim, N. H. A., Soh, Z. H. C., Osman, M. K., & Saad, Z. (2024) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce61582.2024.10696059 · Cited by 2

Authors: Loey, M., Smarandache, F., & M. Khalifa, N. E.
Affiliations: Benha University (EG)
Department of Mathematics, University of New Mexico, Gallup Campus, NM 87301, USA
Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Cairo 12613, Egypt
Corresponding author: Loey, Mohamed
ORCID: Loey, Mohamed
Smarandache, Florentin
M. Khalifa, Nour Eldeen
Venue: Symmetry
Publisher: MDPI AG
Published: 2020-04-20
Volume: 12
Issue: 4
Pages: 651
ISSN: 2073-8994 (ISSN-L)
DOI: 10.3390/sym12040651
OpenAlex: W3016488464
Cited by: 538
References: 62
Citations by year: 2026: 7 · 2025: 33 · 2024: 54 · 2023: 85 · 2022: 146 · 2021: 156 · 2020: 56
FWCI: 53.94
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Symmetry (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: COVID-19 detection, chest X-ray images, generative adversarial networks, data augmentation, transfer learning, pneumonia classification, bacterial pneumonia, viral pneumonia, AlexNet, GoogLeNet, ResNet-18

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

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks