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Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

Ter-Sarkisov, A. ORCID: 0000-0002-1300-6132 (2021). Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19. Science in Information and Technology Letters, 1(2), pp. 92-99. doi: 10.31763/sitech.v1i2.202

Abstract

This paper compares the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19. We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately. The best model achieves the mean average precision of 44.68% using MS COCO criterion on the segmentation across all accuracy thresholds. The classification model, COVID-CT-Mask-Net, learns to predict the presence of COVID-19 vs. common pneumonia vs. control. The model achieves the 93.88% COVID-19 sensitivity, 95.64% overall accuracy, 95.06% common pneumonia sensitivity, and 96.91% true-negative rate on the COVIDx-CT test split (21192 CT scans) using a small fraction of the training data. We also analyze the effect of the Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy. The full source code, models, and pre-trained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.

Publication Type: Article
Additional Information: This is an open access article under the CC–BY-SA license.
Publisher Keywords: COVID-19, Lesion Segmentation, Pneumonia Classification, Mask R-CNN
Subjects: H Social Sciences > HN Social history and conditions. Social problems. Social reform
Q Science > QR Microbiology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date available in CRO: 10 Dec 2021 08:52
Date deposited: 10 December 2021
Date of acceptance: 20 January 2021
Date of first online publication: 21 January 2021
URI: https://openaccess.city.ac.uk/id/eprint/27228
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