A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
The article presents a comparison of various deep learning architectures for classifying COVID-19 using CT and X-ray images. It highlights the effectiveness of convolutional neural networks in distinguishing between COVID-19 and healthy lung images. The study reports high accuracy rates for certain architectures, surpassing previous findings in the field.
- ▪The research focuses on using artificial intelligence to automate the diagnosis of COVID-19 through imaging techniques.
- ▪Different pre-trained networks, including Resnet and VGG, achieved an accuracy of 95 to 98 percent in classification tasks.
- ▪The study utilized multiple datasets of X-ray and CT images to evaluate the performance of the deep learning models.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20445 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 19 May 2026] Title:A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery Authors:Sarmad Khan, Arslan Shaukat, Umer Asgher, Basim Azam View a PDF of the paper titled A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery, by Sarmad Khan and 3 other authors View PDF HTML (experimental)…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
