Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
The research paper presents a comparative analysis of automated image segmentation architectures for predicting COVID-19 lesions in CT imagery. It evaluates various deep learning frameworks and pre-trained backbones to enhance segmentation accuracy. The findings indicate that these architectures can achieve high precision in both binary and multi-class segmentation tasks.
- ▪The study integrates four deep learning architectures with six pre-trained encoders for image segmentation.
- ▪A maximum F1-Score of 98% was achieved for binary class segmentation.
- ▪Multi-class segmentation yielded F1-Scores of 75% and 77% across two datasets.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20459 |
| 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 |
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| 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 | xZF0UAveHE16 |
| 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 |
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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.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20459 (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:Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures Authors:Sarmad Khan, Arslan Shaukat, Umer Asgher, Basim Azam View a PDF of the paper titled Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures, by Sarmad Khan and 3 other authors View…
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