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