Comparative evaluation of UNet, PSPNet, LinkNet and FPN with six encoders on three COVID-19 CT datasets reports up to 98% F1 for binary lesion segmentation and 75-77% for multi-class.
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2026 2verdicts
UNVERDICTED 2representative citing papers
ResNet and VGG models achieve 95-98% average accuracy distinguishing COVID-19 from normal lung images on X-ray and CT datasets using transfer learning from pre-trained networks.
citing papers explorer
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Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
Comparative evaluation of UNet, PSPNet, LinkNet and FPN with six encoders on three COVID-19 CT datasets reports up to 98% F1 for binary lesion segmentation and 75-77% for multi-class.
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A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
ResNet and VGG models achieve 95-98% average accuracy distinguishing COVID-19 from normal lung images on X-ray and CT datasets using transfer learning from pre-trained networks.