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arXiv AI By Sarmad Khan, Basim Azam, Arslan Shaukat

Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures

Read the original on arXiv AI →

The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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