Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
; : 615-621, 2022.
Article
in English
| Scopus | ID: covidwho-1962418
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 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. © 2022 ACM.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
15th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2022
Year:
2022
Document Type:
Article
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