Electrical Engineering and Systems Science > Image and Video Processing
[Submitted on 4 May 2022 (v1), last revised 20 May 2022 (this version, v4)]
Title:Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
View PDFAbstract: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.
Submission history
From: Constantine Maganaris [view email][v1] Wed, 4 May 2022 16:15:25 UTC (442 KB)
[v2] Wed, 18 May 2022 06:40:37 UTC (459 KB)
[v3] Thu, 19 May 2022 07:18:21 UTC (459 KB)
[v4] Fri, 20 May 2022 10:52:27 UTC (459 KB)
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