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.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20445 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1FVebeckhMQu |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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)…
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