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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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Computer Vision and Pattern Recognition arXiv:2605.20459 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as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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