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Recent studies indicate that detecting radiographic patterns on CT scans canyield high sensitivity and specificity for Covid-19 localization. In thispaper, we investigate the appropriateness of deep learning modelstransferability, for semantic segmentation of pneumonia-infected areas in CTimages. Transfer learning allows for the fast initialization/reutilization ofdetection models, given that large volumes of training data are not available.Our work explores the efficacy of using pre-trained U-Net architectures, on aspecific CT data set, for identifying Covid-19 side-effects over images fromdifferent datasets. Experimental results indicate improvement in thesegmentation accuracy of identifying Covid-19 infected regions.

Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models