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

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

Deep learning disease prediction model for use with intelligent robots

Koppu, S., Maddikunta, P. K. R., & Srivastava, G. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106765 · Cited by 48

COVID-19 Modeling: A Review

Cao, L., & Liu, Q. (2024) · ACM Computing Surveys · doi:10.1145/3686150 · Cited by 52

SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays

Makkar, A., & Santosh, K. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01789-7 · Cited by 56

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset

Khalifa, N. E. M., Smarandache, F., Manogaran, G., & Loey, M. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09802-9 · Cited by 47

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Chiroma, H., Ezugwu, A. E.-S., Jauro, F., Al-Garadi, M. A., Abdullahi, I. N., & Shuib, L. M. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.313 · Cited by 49

Federated learning based Covid‐19 detection

Chowdhury, D., Banerjee, S., Sannigrahi, M., Chakraborty, A., Das, A., Dey, A., & Dwivedi, A. D. (2022) · Expert Systems · doi:10.1111/exsy.13173 · Cited by 52

Local binary pattern and deep learning feature extraction fusion for COVID‐19 detection on computed tomography images

Mubarak, A. S., Serte, S., Al‐Turjman, F., Ameen, Z. S., & Özsöz, M. (2021) · Expert Systems · doi:10.1111/exsy.12842 · Cited by 47

Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection

Sitaula, C., Shahi, T. B., Aryal, S., & Marzbanrad, F. (2021) · Scientific Reports · doi:10.1038/s41598-021-03287-8 · Cited by 45

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

Xu, Z., Su, C., Xiao, Y., & Wang, F. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.09.001 · Cited by 48

Using artificial intelligence technology to fight COVID-19: a review

Peng, Y., Liu, E., Peng, S., Chen, Q., Li, D., & Lian, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10106-z · Cited by 47

Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images

Sharma, A., & Mishra, P. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13486-8 · Cited by 50

Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

Islam, M. M., Islam, M. Z., Asraf, A., Al-Rakhami, M. S., Ding, W., & Sodhro, A. H. (2022) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2023.100088 · Cited by 52

A novel deep learning based method for COVID-19 detection from CT image

Moghaddam, S. M. J., & Gholamalinejad, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102987 · Cited by 44

Building Resilience against COVID-19 Pandemic Using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress

Abir, S. M. A. A., Islam, S. N., Anwar, A., Mahmood, A. N., & Oo, A. M. T. (2020) · IoT · doi:10.3390/iot1020028 · Cited by 44

COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model

Irmak, E. (2022) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-022-01102-w · Cited by 44

X‐Ray Equipped with Artificial Intelligence: Changing the COVID‐19 Diagnostic Paradigm during the Pandemic

Ghaderzadeh, M., Aria, M., & Asadi, F. (2021) · BioMed Research International · doi:10.1155/2021/9942873 · Cited by 43

Implementation of convolutional neural network approach for COVID-19 disease detection

Irmak, E. (2020) · Physiological Genomics · doi:10.1152/physiolgenomics.00084.2020 · Cited by 42

Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Hariri, W., & Narin, A. (2021) · Soft Computing · doi:10.1007/s00500-021-06137-x · Cited by 44

An XAI approach for COVID-19 detection using transfer learning with X-ray images

Sarp, S., Çatak, F. Ö., Kuzlu, M., Cali, Ü., Kusetoğulları, H., Zhao, Y., Ateş, G., & Güler, Ö. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15137 · Cited by 47

COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19Preprint

Siddhartha, M., & Santra, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13873 · Cited by 67

An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings

Al-Timemy, A. H., Khushaba, R. N., Mosa, Z. M., & Escudero, J. (2021) · Studies in Systems, Decision and Control · doi:10.1007/978-3-030-69744-0_6 · Cited by 41

An Integrated Approach for Monitoring Social Distancing and Face Mask Detection Using Stacked ResNet-50 and YOLOv5

Walia, I. S., Kumar, D., Sharma, K., Hemanth, D. J., & Popescu, D. E. (2021) · Electronics · doi:10.3390/electronics10232996 · Cited by 41

A multiclass deep learning algorithm for healthy lung, Covid-19 and pneumonia disease detection from chest X-ray images

Mohan, G., Subashini, M. M., Balan, S., & Singh, S. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00110-x · Cited by 44

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

Maior, C. B. S., Santana, J. M., Lins, I. D., & Moura, M. J. C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247839 · Cited by 45

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Barua, P. D., Gowdh, N. F. M., Rahmat, K., Ramli, N. M., Ng, W. L., Chan, W. Y., Kuluöztürk, M., Doğan, Ş., Bayğın, M., Yaman, O., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18158052 · Cited by 44

A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds

Aly, M., & Alotaibi, N. S. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101049 · Cited by 42

New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis

Sitaula, C., & Aryal, S. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00152-w · Cited by 41

DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy

Wang‎, S., Zhang, X., & Zhang, Y. (2021) · ACM Transactions on Management Information Systems · doi:10.1145/3451357 · Cited by 39

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans

El-Bana, S., Al-Kabbany, A., & Sharkas, M. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.303 · Cited by 41

The application of industry 4.0 technologies in pandemic management: Literature review and case study

Moosavi, J., Bakhshi, J., & Martek, I. (2021) · Healthcare Analytics · doi:10.1016/j.health.2021.100008 · Cited by 43

Five Strategies for Bias Estimation in Artificial Intelligence-based Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients using AP(ai)Bias 2.0: A Systematic Review

Suri, J. S., Agarwal, S., Jena, B., Saxena, S., El-Baz, A., Agarwal, V., Kalra, M. K., Saba, L., Viskovic, K., Fatemi, M., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3174270 · Cited by 38

A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images

Rezaee, K., Badiei, A., & Meshgini, S. (2020) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme51989.2020.9319426 · Cited by 37

COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3

Shadin, N. S., Sanjana, S., & Lisa, N. J. (2021) · doi:10.1109/icit52682.2021.9491752 · Cited by 40

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

Nyangaresi, V. O., El-Omari, N. K. T., & Nyakina, J. N. (2022) · Journal of Computer Science Research · doi:10.30564/jcsr.v4i1.3852 · Cited by 38

An ensemble approach for multi-stage transfer learning models for COVID-19 detection from chest CT scans

Cruz, J. F. H. S. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100027 · Cited by 38

A Deep-Learning-Based Edge-Centric COVID-19-Like Pandemic Screening and Diagnosis System within a B5G Framework Using Blockchain

Muhammad, G., & Hossain, M. S. (2021) · IEEE Network · doi:10.1109/mnet.011.2000326 · Cited by 36

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

Liang, X., Zhang, Y., Wang, J., Ye, Q., Liu, Y., & Tong, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2020.612962 · Cited by 35

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Nikolaou, V., Massaro, S., Fakhimi, M., Stergioulas, L. K., & Garn, W. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00166-4 · Cited by 38

Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images

Salih, S. Q., Abdulla, H. K., Ahmed, Z. S., Surameery, N. M. S., & Rashid, R. D. (2020) · Kurdistan Journal of Applied Research · doi:10.24017/covid.14 · Cited by 35

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Gürsoy, E., & Kaya, Y. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01083-0 · Cited by 40

Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy

Alsattar, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Martínez, L. M., Pamucar, D. S., & Pedrycz, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121300 · Cited by 34

Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

Alsaade, F. W., Aldhyani, T. H. H., & Al-Adhaileh, M. H. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016264 · Cited by 34

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Abdelhamid, A., AbdElhalim, E., Mohamed, M. A., & Khalifa, F. (2022) · Applied Sciences · doi:10.3390/app12042080 · Cited by 36

CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image

Zhang, P., Zhong, Y., Deng, Y., Tang, X., & Li, X. (2020) · Diagnostics · doi:10.3390/diagnostics10110901 · Cited by 33

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar, H., & Ceylan, M. (2020) · Applied Intelligence · doi:10.1007/s10489-020-02019-1 · Cited by 33

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review

Montazeri, M., ZahediNasab, R., Farahani, A., Mohseni, H., & Ghasemian, F. (2021) · JMIR Medical Informatics · doi:10.2196/25181 · Cited by 31

Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm

Absar, N., Mamur, B., Mahmud, A., Emran, T. B., Khandaker, M. U., Faruque, M. R. I., Osman, H., Elzaki, A. A., & Elkhader, B. A. (2022) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2022.02.002 · Cited by 33

A comprehensive review of COVID-19 detection with machine learning and deep learning techniques

Das, S., Ayus, I., & Gupta, D. (2023) · Health and Technology · doi:10.1007/s12553-023-00757-z · Cited by 37

Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays

Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E., & Soda, P. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108242 · Cited by 33

Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning

Ji, D., Zhang, Z., Zhao, Y., & Zhao, Q. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6799202 · Cited by 34

COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm

Poola, R. G., Lahari, P. L., & Siva Sankar, Y. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101020 · Cited by 36

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

Zulkifley, M. A., Abdani, S. R., & Zulkifley, N. H. (2020) · Applied Sciences · doi:10.3390/app10207233 · Cited by 31

COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled

Vinod, D. N., & Prabaharan, S. R. S. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09882-4 · Cited by 31

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

Benifa, J. V. B., Chola, C., Muaad, A. Y. M., Hayat, M. A. B., Heyat, M. B. B., Mehrotra, R., Akhtar, F., Hussein, H. S., Vargas, D. L. R., Castilla, Á. K., et al. (2023) · Sensors · doi:10.3390/s23136090 · Cited by 30

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

Ahrabi, S. S., Scarpiniti, M., Baccarelli, E., & Momenzadeh, A. (2021) · Computation · doi:10.3390/computation9010003 · Cited by 31

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

Aldhyani, T. H. H., Alrasheed, M., Al-Adhaileh, M. H., Alqarni, A. A., Alzahrani, M. Y., & Alahmadi, A. H. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.014498 · Cited by 30

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Karthik, R., Menaka, R., Hariharan, M., & Kathiresan, G. S. (2021) · IRBM · doi:10.1016/j.irbm.2021.07.002 · Cited by 30

Battling COVID-19 using machine learning: A review

Chadaga, K., Prabhu, S., Vivekananda Bhat, K., Niranjana, S., & Umakanth, S. (2021) · Cogent Engineering · doi:10.1080/23311916.2021.1958666 · Cited by 30

A multi-modal bone suppression, lung segmentation, and classification approach for accurate COVID-19 detection using chest radiographs

Rani, G., Misra, A., Dhaka, V. S., Buddhi, D., Sharma, R. K., Zumpano, E., & Vocaturo, E. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200148 · Cited by 30

Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis

Alhashmi, S. M., Hashem, M., & Al-Qudah, I. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200299 · Cited by 44

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs

Arora, R., Bansal, V., Buckchash, H., Kumar, R., Sahayasheela, V. J., Narayanan, N., Pandian, G. N., & Raman, B. (2021) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-021-01060-9 · Cited by 32

COVID-19 Diagnosis using X-Ray Images and Deep learning

Sharma, S., & Tiwari, S. (2021) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais50930.2021.9395851 · Cited by 29

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

Al-Shargabi, A. A., Alshobaili, J. F., Alabdulatif, A., & Alrobah, N. (2021) · Applied Sciences · doi:10.3390/app11167174 · Cited by 30

Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model

Vinod, D. N., Jeyavadhanam, B. R., Zungeru, A. M., & Prabaharan, S. R. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104729 · Cited by 29

CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs

Shentu, J., & Moubayed, N. A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00513 · Cited by 30

COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach

Khriji, L., Ammari, A. C., Messaoud, S., Bouaafia, S., Maraoui, A., & Machhout, M. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct52173.2021.9435454 · Cited by 28

Deep Learning for Covid-19 Screening Using Chest X-Rays in 2020: A Systematic Review

Santosh, K., Ghosh, S., & Roy, D. G. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422520103 · Cited by 30

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

Rasheed, J., & Shubair, R. M. (2022) · Healthcare · doi:10.3390/healthcare10071313 · Cited by 28

COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers

Costa, Y. M. G., Silva, S. A., Teixeira, L. O., Pereira, R. M., Bertolini, D. S., Britto, A. S., Oliveira, L. S., & Cavalcanti, G. D. C. (2022) · Sensors · doi:10.3390/s22197303 · Cited by 29

Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting

Chen, L., Qiao, C., Wu, M., Cai, L., Yin, C., Yang, M., Sang, X., & Bai, W. (2023) · Bioengineering · doi:10.3390/bioengineering10020184 · Cited by 27

Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease

Senan, E. M., Alzahrani, A., Alzahrani, M. Y., Alsharif, N. H., & Aldhyani, T. H. H. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/6919483 · Cited by 27

Automated detection of COVID-19 from X-ray images using CNN and Android mobile

Bushra, K. F., Ahamed, M. A., & Ahmad, M. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00163-2 · Cited by 27

ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection

Ghosh, S. K., & Ghosh, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103286 · Cited by 27

BEMD-3DCNN-based method for COVID-19 detection

Riahi, A. A., Elharrouss, O., Al-Maadeed, S. A., & Al-Maadeed, S. A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105188 · Cited by 29

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

Khan, M. H. R., & Hossain, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.561801 · Cited by 25

Detection of COVID ‐19 from chest X‐ray images: Boosting the performance with convolutional neural network and transfer learning

Asif, S., Yi, W., Amjad, K., Hou, J., Tao, Y., & Si, J. (2022) · Expert Systems · doi:10.1111/exsy.13099 · Cited by 27

Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture

Ghose, P., Uddin, M. A., Acharjee, U. K., & Sharmin, S. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200130 · Cited by 31

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

Ali, H., & Shah, Z. (2022) · JMIR Medical Informatics · doi:10.2196/37365 · Cited by 25

Point-of-Care Devices for Viral Detection: COVID-19 Pandemic and Beyond

Yadav, S. K., Verma, D., Yadav, U., Kalkal, A., Priyadarshini, N., Kumar, A., & Mahato, K. (2023) · Micromachines · doi:10.3390/mi14091744 · Cited by 25

Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data

Ding, W., Nayak, J., Swapnarekha, H., Abraham, A., Naik, B., & Pelusi, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.024 · Cited by 24

Coevolutionary generative adversarial networks for medical image augumentation at scale

Flores, D. J., Hemberg, E., Toutouh, J., & O'Reily, U.-M. (2022) · Annual Conference on Genetic and Evolutionary Computation · doi:10.1145/3512290.3528742 · Cited by 17

A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms

Pradhan, A. K., Mishra, D., Das, K. P., Obaidat, M. S., & Kumar, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13826-8 · Cited by 25

A Comparative Study of CNN Transfer Learning Classification Algorithms with Segmentation for COVID-19 Detection from CT Scan Images

Seum, A., Raj, A. H., Sakib, S., & Hossain, T. (2020) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece51571.2020.9393129 · Cited by 22

Design of an artificial neural network to predict mortality among COVID-19 patients

Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100983 · Cited by 21

A lightweight CORONA-NET for COVID-19 detection in X-ray images

Hadi, M. U., Qureshi, R., Ahmed, A., & Iftikhar, N. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120023 · Cited by 24

Deep Learning and Federated Learning for Screening COVID-19: A Review

Mondal, M. R. H., Bharati, S., Podder, P., & Kamruzzaman, J. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3030045 · Cited by 21

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal

Althenayan, A. S., Alsalamah, S., Aly, S. F., Nouh, T. A., Mahboub, B. H., Salameh, L. I. J., Alkubeyyer, M., & Mirza, A. A. (2024) · Sensors · doi:10.3390/s24082641 · Cited by 20

Developing an artificial neural network for detecting COVID-19 disease

Shanbehzadeh, M., Nopour, R., & Kazemi-Arpanahi, H. (2022) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_387_21 · Cited by 19

SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

Smadi, A. A., Abugabah, A., Al-smadi, A. M., & Almotairi, S. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101059 · Cited by 20

Covid Detection from CXR Scans using Deep Multi-layered CNN

Bhadra, R., & Kar, S. (2020) · IEEE Bombay Section Signature Conference (IBSSC) · doi:10.1109/ibssc51096.2020.9332210 · Cited by 19

A Novel Deep Learning Pipeline Architecture based on CNN to Detect Covid-19 in Chest X-ray Images

Putra Sumari, S. J. S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i6.4804 · Cited by 19

Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging

Kong, H., Kim, J. Y., Moon, H.-M., Park, H. C., Kim, J., Lim, R., Woo, J., Fakhri, G. E., Kim, D. W., & Kim, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-22222-z · Cited by 18

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

Agnihotri, A., & Kohli, N. (2023) · Data Science and Management · doi:10.1016/j.dsm.2023.03.005 · Cited by 18

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Achicanoy, H., Chaves, D., & Trujillo, M. (2021) · Symmetry · doi:10.3390/sym13081497 · Cited by 17

Transfer learning with NASNet-Mobile for Pneumonia X-ray classification

Naskinova, I. (2022) · Asian-European Journal of Mathematics · doi:10.1142/s1793557122502400 · Cited by 20

A residual network-based framework for COVID-19 detection from CXR images

Kibriya, H., & Amin, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08127-y · Cited by 18

A deep convolution neural network for automated COVID-19 disease detection using chest X-ray images

Thangaraj, R., Pandiyan, P., Ramakrishnan, J., Nallakumar, R., & Eswaran, S. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100278 · Cited by 19

A Deep Transfer Learning Approach to Diagnose Covid-19 using X-ray Images

Progga, N. I., Hossain, M. S., & Andersson, K. (2020) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece52138.2020.9398037 · Cited by 18

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images

Fang, L., & Wang, X. (2022) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2022.07.009 · Cited by 17

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

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 188

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

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