Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
Researchers have evaluated deep learning architectures for predicting COVID-19 lesions in CT scans, addressing the lack of standardized performance analysis in medical image segmentation. The study integrated four segmentation frameworks (Unet, PSPNet, Linknet, FPN) with six pre-trained encoders to create diverse testing architectures. Analysis across three COVID-19 CT datasets showed high precision, with a maximum F1-Score of 98% for binary segmentation and scores of 75% and 77% for multi-class segmentation, demonstrating AI's enhancement of pandemic disease diagnostics. AI
IMPACT Demonstrates improved diagnostic accuracy for pandemic diseases through AI-driven medical image analysis.