The DOI matches its Crossref record; 130 of the 141 references it cites were found in a registry or an index, and 1 are not checked yet.
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Open accessAbstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
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“Nevertheless, the ‘black-box’ nature [168], dependency on large labeled datasets, and high computational cost hinder their widespread clinical implementation.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [168]
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Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey
Siddiqi, R., & Javaid, S. (2024) · Journal of Imaging · doi:10.3390/jimaging10080176 · Cited by 145
Automated detection and forecasting of COVID-19 using deep learning techniques: A review
Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Sadeghi, D., Moridian, P., Khadem, A. A., Alizadehsani, R., Hussain, S., Zare, A., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127317 · Cited by 306
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Ullah, N., Khan, J. A., El–Sappagh, S., El-Rashidy, N. M., & Khan, M. S. (2023) · Diagnostics · doi:10.3390/diagnostics13010162 · Cited by 73
Metadata
- Authors
- Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D.
- Affiliations
- Indian Institute of Technology Delhi (IN)
- CMR University (IN)
- National Institute of Technology Hamirpur (BD)
- Indraprastha Institute of Information Technology Delhi (IN)
- Corresponding author
- Singh, Pushpendra
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 144
- Pages
- 105350
- ISSN
- 0010-4825 (ISSN-L)
- PMC
- PMC8890789
- OpenAlex
- W4214860167
- Cited by
- 168
- References
- 141
- Citations by year
- 2026: 21 · 2025: 35 · 2024: 43 · 2023: 46 · 2022: 23
- FWCI
- 6.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 pandemic, SARS-CoV-2, chest radiography, computed tomography, image classification, convolutional neural networks, automated diagnosis, medical image analysis, deep learning
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Artificial Intelligence, Humans, Neural Networks, Computer
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