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

• URL: http://arxiv.org/abs/2205.02152v1
• Date: Wed, 4 May 2022 16:15:25 GMT
• Title: Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
• Authors: Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos, Nikolaos Doulamis, Dimitris Kalogeras and Aikaterini Angeli
• Abstract summary: In this paper, we investigate the appropriateness of deep learning models transferability, for semantic segmentation of pneumonia-infected areas in CT images. 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.
• Score: 8.026717228180935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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.

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