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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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ImageNet: A large-scale hierarchical image database

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Densely Connected Convolutional Networks

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Going deeper with convolutions

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Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138

A novel coronavirus from patients with pneumonia in China, 2019

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513

Lung Pathology Detection: A Qualitative and Comprehensive Survey of Deep Neural Networks Utilising CT, MRI, and PET Imaging Modalities

Assish, Y. S. S. K., & Kuppusamy, P. (2025) · Journal of Innovative Image Processing · doi:10.36548/jiip.2025.4.002 · Cited by 1

Machine learning-based early detection of tuberculosis in asymptomatic high-risk populations

Joshi, H. D., Barhate, M. Y., More, K. P., Abin, D., Murumkar, R., & Singh, V. K. (2025) · Indian Journal of Tuberculosis · doi:10.1016/j.ijtb.2025.10.005 · Cited by 1

Enhancing classification robustness: Stability analysis of fuzzy and shannon entropy of image under noisy environment

Khan, F., Islam, M. I., & Jahan, S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04971-2 · Cited by 1

Hacia un modelo universal de IA para la detección simultánea de COVID-19, neumonía bacteriana y tuberculosis en imágenes de rayos X: Desafíos y oportunidades

Candia-Herrera, D. C., Mojica, K. Y. S., Zarama-Ortiz, S. L., & Diaz-Mesa, C. L. (2026) · Respuestas · doi:10.22463/0122820x.5433 · Cited by 1

A Robust Approach for Malaria Parasite Identification with CNN Based Feature Extraction and Classification using SVM

Ohdar, K., & Nigam, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306840 · Cited by 1

IEDO-net: Optimized Resnet50 for the classification of COVID-19

Ouyang, C., Wu, H., Shen, J., Zheng, Y., Li, R., Yao, Y., & Li, Z. (2023) · Electronic Research Archive · doi:10.3934/era.2023383 · Cited by 1

Prediction of COVID-19 from Lung Scans Using Deep Learning

Pramanik, A., Sinha, A., & Aluvala, S. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10398007 · Cited by 1

YOLO lung CT disease rapid detection classification with fused attention mechanism

Su, Q., Qin, Z., Mu, J., & Wu, H. (2023) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3650400.3650632 · Cited by 1

Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation

Liang, Z., Xue, Z., Rajaraman, S., & Antani, S. (2024) · New Microbes and New Infections · doi:10.1016/j.nmni.2024.101457 · Cited by 1

Automated and Enhanced Classification of Persea Americana Using Optimized Deep Convolutional Neural Networks With Improved Training Strategies for Agro-Industrial Settings

Ramirez, O. J. V., Cruz, J. E., Huaquipaco, S., Mamani, W., Yana-Mamani, V., & Encinas, S. H. (2024) · IEEE Access · doi:10.1109/access.2024.3496728 · Cited by 1

From lab to pocket: A novel continual learning-based mobile application for screening COVID-19

Falero, D., Kabir, M. A., & Homaira, N. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112960 · Cited by 3

Görüntü Kazıma Yoluyla Oluşturulan Örnek Veri Kümesinin Evrişimsel Sinir Ağı Tabanlı Görüntü Sınıflama Üzerine Etkisinin İncelenmesi

Hayıt, T. (2023) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.1120967 · Cited by 1

A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detectionPreprint

Fki, Z., Ammar, B., Fourati, R., Fendri, H., Daoued, E., Mnif, Z., Hussain, A., & Ayed, M. B. (2022) · Research Square · doi:10.21203/rs.3.rs-2073361/v1 · Cited by 1

MiFL: Multi-Input Neural Networks in Federated LearningPreprint

Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023) · doi:10.36227/techrxiv.22492732.v1 · Cited by 1

A Shallow Learning Investigation for COVID-19 Classification

Zedda, L., Loddo, A., & Ruberto, C. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13321-3_29

Development of Artificial Intelligence Based Clinical Decision Support System on Medical Images for the Classification of COVID-19

Çolak, C., ARSLAN, A. K., UCUZAL, H., Köse, A., Yıldırım, İ. O., Güldoğan, E., & Çolak, M. C. (2022) · Medical Records · doi:10.37990/medr.1130194

Deductive Learning for identifying COVID 19 by using Edge Histogram and Fuzzy Color and Texture Histogram Filter

Manimegalai, S. M., & Ramaprabha, T. (2022) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2022/v13i5/221305182

The Study of Convid-19 Recognition and Performance Optimization Based on AlexNet

Huang, Y. (2022) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v23i.3199

Development of the new Aquicnn algorithm for an augmentation of CT scans images for COVID-19 Patients

Jadhav, S. P. A. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s08.429

Convolutional neural networks for imbalanced chest x-ray images classification

Ouyang, Q. (2023) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1117/12.2656005

Metamorphic Testing for the Medical Image Classification Model

Ma, Y., Pan, Y., & Fan, Y. (2022) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c57518.2022.00057

MiFL: Multi-Input Neural Networks in Federated LearningPreprint

Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023) · doi:10.36227/techrxiv.22492732

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

Ong, J. D. S., Vidad, A. J. P., Valenzuela, I. C., Concepcion, R., Bugtai, N. T., & Naguib, R. N. G. (2022) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem57413.2022.10109522

Covid-19 Detection Using CNN Model with CT Scan Images

Akhilesh, G. L. S., Lokesh, K., Teja, D. V. P. M., Sai, P. J., Tumuluru, P., & Raju, S. H. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128444

Proposing a New Feature Clustering Method in Order to the Binary Classification of COVID-19 in Computed Tomography Images

Balavand, A., & Pahlevani, S. (2023) · Engineering optimization · doi:10.1007/978-981-99-1521-7_11

COVID-19 Diagnosis and Treatment Challenges in Rural India

Chandra, S., Kanojiya, S., Verma, A. R., & Kumar, Y. (2023) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-6684-6418-2.ch011

Automatic statistical diagnosis of COVID-19 based on multi-modal CT feature extraction

Fan, X., Zuo, Z., Li, Y., Zhou, Y., Liu, H., Zhou, X. J., & Zhang, J. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100018

EVALUATION OF THE EFFECTS OF LUNGS CHEST X-RAY IMAGE FUSION WITH ITS WAVELET SCATTERING TRANSFORM COEFFICIENTS ON THE CONVENTIONAL NEURAL NETWORK CLASSIFIER ACCURACY DURING THE COVID-19 DISEASE

Arvanaghi, R., & Meshgini, S. (2023) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237223500199

Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images

Et al. Vidyasri, S. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8636

Research on Image Database Classification Method Based on MHPA

Tang, W., Li, L., Zhao, W., & Yang, R. (2023) · International Conference on Electronics and Devices, Computational Science (ICEDCS) · doi:10.1109/icedcs60513.2023.00059

Application of Generative Adversarial Networks for Synthetic COVID-19 Ultrasound Data Generation

Silva, P., de Sá, A. M. F. L. M., Félix, L. B., Pereira, W. C. D. A., & Seixas, J. M. (2024) · Congresso Brasileiro de Inteligência Computacional (CBIC) · doi:10.21528/cbic2023-033

Aiding from Deep Learning Applications in the Classification of Medical Images

Behery, G. M., Farouk, R., Ahmed, E., & Ali, A. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47718-8_37

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization

Sagar, D., Dwivedi, T., Gupta, A., Aggarwal, P., Bhatnagar, S., Mohan, A., Kaur, P., & Gupta, R. (2024) · Cureus · doi:10.7759/cureus.57336

Covid-19 Pneumonia Chest X-Ray Pattern Synthesis by Stable Diffusion

Liang, Z., Xue, Z., Rajaraman, S., & Antani, S. (2024) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai59505.2024.10508671 · Cited by 4

Comparison of Convolutional Neural Networks in SARS-CoV-2 Identification

Ramaphosa, K. I. M., Zuva, T., & Otunniyi, T. O. (2024) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict62343.2024.00024

Optimizing Pneumonia Detection from Scarce Chest X-Ray Data: A Comparative Analysis of Advanced Augmentation Techniques Using Deep Learning

Sabha, S. U., Din, N. M. U., Assad, A., & Bhat, M. R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3292-0_5

Deep Hybrid Learning Approaches for COVID-19 Virus Detection Using Chest X-ray Images

Alohali, M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150711

E-Commerce Data Alchemy

Bonkra, A., Dhiman, P., & Shah, M. A. (2024) · Advances in electronic commerce (AEC) book series/Advances in electronic commerce series · doi:10.4018/979-8-3693-5718-7.ch008

An Ensemble based Convolutional Neural Network modelling for Classifying Marine Animal types

Charitha, T., Sagi, S., Samitha, V., & Kavitha, C. R. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725155

Detection and Classification of COVID-19 and Pneumonia Using Chest X-Ray Images

Indhumathi, R., Aurchana, P., Nirmaladevi, K., Sowmya, S., Ramalingam, A., & Revathy, G. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-8355-7_37

Artificial intelligence based systems for combating communicable diseases

Saurav, & Kumar, T. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02735-2

Adaptive Convolutional Neural Network Integrating Sequential Memory and Transfer Learning for CT-Based COVID-19 Detection

Hosseinpoor, M. (2025) · Brilliant Engineering · doi:10.36937/ben.2025.41003

Prototype Pandemic Defense System: CNN-Powered Medical Imaging for Disease Prediction

Konda, O., Rajeev, N., Mohammad, R. A. S., & Kamal, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03527-1_39

COVID-19 Prediction of Chest Computerized Tomography Images Using Convolutional Neural Networks

Ramaphosa, K. I. M., Zuva, T., & Otunniyi, T. O. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00715-5_10

Neural-network learning of two-phase flow regimes from EIT-spectrograms

SAOUDI, A., Bellis, C., Darnajou, M., Ricciardi, G., & Mylvaganam, S. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae1aa0

Adaptive dual-window enhancement and multi-scale texture prior fusion for robust kidney CT classification

Xia, P., Li, Y., Yao, X., Jiang, Y., Wei-Ming, H., & Wei, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0335585

Application of Generative Adversarial Networks to Improve COVID-19 Classification on Ultrasound Images

Silva, P. S. T. F., de Sá, A. M. F. L. M., Pereira, W. C. D. A., Félix, L. B., & Seixas, J. M. (2025) · Journal of Imaging · doi:10.3390/jimaging11120451

Deep semi-supervised regression with cooperative uncertainty-consistency regularization and adaptive calibration

Zhou, X., Jiang, M., Zhao, Y., Pang, L., & Wang, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131276

Enhanced Detection and Classification of Pulmonary Diseases Using an Ensembled Convolutional Neural Network Architecture on Chest X-Ray Images

Khiem, N. M., Quyen, P. N., & Thang, L. D. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4721-0_20

Impact of Adaptive Learning Rate Strategies on Deep Learning Models for Chest X-Ray Diagnosis

Moqbel, M. A. A. A., Saad, Z., Maruzuki, M. I. F., Osman, N. D., Damit, D. S. A., & Soh, Z. H. C. (2025) · doi:10.1109/iceei68459.2025.11331111

A Hybrid CNN-ViT COVID-19 Detection Framework with Self-Supervised Learning and Explainability for Chest CT Images

Kushwaha, S. (2025) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit66109.2025.11437071

Developing a Novel Knowledge Distillation Method for Improved Medical Image Classification

Nandhini, R., & Shunmughavel, V. (2026) · International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) · doi:10.1109/icauc68182.2026.11441079

A hybrid feature selection framework collaborating image processing, evolutionary intelligence, and machine learning for covid-19 disease classification

Singh, L. K., Khanna, M., Yadav, A. K., & Singh, R. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00786-4

Fusion for Classification of COVID-19 Patients Using a Discriminative Shape–Texture Convolutional Neural Network with X-Ray Images and Speech Signal Processing

Venkatesh, R., Shenbagarajan, A., & Shenbagalakshmi, G. (2026) · Journal of Bionic Engineering · doi:10.1007/s42235-026-00866-1

Semantic-preserving augmentation with saliency-aware geometric fusion for long-tailed visual recognition

Zeng, W., Li, M., Lin, Z., & Mao, G. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.106050

Captioning of Images Using ResNet50 and MobileNet: A Mixed Model Approach

Dhar, M., Jena, A. R., Bhattacharjee, S., Sen, M., Chakraborty, B., Nandy, D., & Barman, B. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-21164-4_7

AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness

Ma, L., Zhao, J., Luan, J., & Zhang, L. (2026) · Frontiers in Chemistry · doi:10.3389/fchem.2026.1842082

Metaheuristic-Optimized Diverse Enhanced Capsule Network for Accurate COVID-19 Detection from Clinical X-ray and CT Images

Manikandan, R., & Maruthai, K. (2025) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs65898.2025.11581066

Comparative Evaluation of YOLOv8, YOLOv11 and ResNet32 for Deep Learning-Based COVID-19 Classification from Chest X-ray Images

Balcı, B., & Başçi̇ftçi̇, F. (2026) · Türk doğa ve fen dergisi :/Türk doğa ve fen dergisi · doi:10.46810/tdfd.1871343

Comparative Analysis of Logistic Regression, Random Forest, XGBoost, LightGBM, and Voting Classifier for Early Stroke Detection

Sharma, A., Ahmed, S. U., Aziz, R. M., & Shah, M. A. (2026) · Studies in Rhythm Engineering · doi:10.1007/978-981-95-8938-8_3

A High-Accuracy Multimodal Deep and Generative Learning Framework for Robust COVID-19 Variant Detection Using Chest X-Ray and CT Imaging

Srinivasulu, A., Agrawal, A., Kamma, P., Ponnada, V. T., & Vedaiyan, R. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1016401

Leveraging Deep Learning Techniques for Automated COVID-19 Screening in Healthcare

Kumar, S. A., Maini, E., Priyadarshini, S. H., & Supriya, J. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-981-95-9101-5_45

D3Fuzz: Dynamic, directed and decaying LLM-driven fuzzing for deep learning libraries

Sun, J., Dong, J., Zhang, H., Tian, Z., & Zhang, Y. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108289

AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis

Didi, Y., Walha, A., & Wali, A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12457-6

Deep Learning-Based Image Classification for COVID-19 Disease: A Comprehensive Review

Ghosh, S., Chhetri, Y., Das, K., Mitra, A., Dutta, P., & Adak, P. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00286-6

A comprehensive review of AI-based approaches for lung disease classification using chest X-ray images

Didi, Y., Walha, A., & Wali, A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21915-1

Neural Gas Network-Based Distinction Between Brain Cysts and Tumors from Magnetic Resonance Images Using a Hybrid Deep Learning Algorithm

Yağanoğlu, M., Özaltın, Ö., & Coşkun, O. (2026) · Big Data · doi:10.1177/2167647x261490062

Искусственный интеллект в прогнозировании сердечно-сосудистых осложнений сахарного диабета 2-го типа: обзор прогностических моделей, их интерпретируемости и клинической применимости

Адылова, Ф. Т., Тригулова, Р. Х., Раxимович, Д. Р., & Хайдарова, Г. А. (2026) · Кардиология в Беларуси · doi:10.34883/pi.2026.18.5.010

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques

Subramaniam, K., Palanisamy, N., Sinnaswamy, R. A., Muthusamy, S., Mishra, O. P., Kumar, L. A., Ramamoorthi, P., Rajan, C. C. A., Thangavel, G., & Sundararajan, S. C. M. (2023) · Soft Computing · doi:10.1007/s00500-023-08561-7 · Cited by 31

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

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

Deep Learning-Based Image Classification for COVID-19 Disease: A Comprehensive Review

Ghosh, S., Chhetri, Y., Das, K., Mitra, A., Dutta, P., & Adak, P. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00286-6

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102365 · Cited by 401