Explained deep learning framework for COVID-19 detection in volumetric CT images aligned with the British Society of Thoracic Imaging reporting guidance : a pilot study
Fouad, Shereen Usman, Muhammad Kabir, Ra'eesa Rajasekaran, Arvind Morlese, John Nagori, Pankaj
Fouad, Shereen
Usman, Muhammad
Kabir, Ra'eesa
Rajasekaran, Arvind
Morlese, John
Nagori, Pankaj
Abstract
In March 2020, the British Society of Thoracic Imaging (BSTI) introduced a reporting guidance for COVID-19 detection to streamline standardised reporting and enhance agreement between radiologists. However, most current DL methods do not conform to this guidance. This study introduces a multi-class deep learning (DL) model to identify BSTI COVID-19 categories within CT volumes, classified as 'Classic', 'Probable', 'Indeterminate', or 'Non-COVID'. A total of 56 CT pseudoanonymised images were collected from patients with suspected COVID-19 and annotated by an experienced chest subspecialty radiologist following the BSTI guidance. We evaluated the performance of multiple DL-based models, including three-dimensional (3D) ResNet architectures, pre-trained on the Kinetics-700 video dataset. For better interpretability of the results, our approach incorporates a post-hoc visual explainability feature to highlight the areas of the image most indicative of the COVID-19 category. Our four-class classification DL framework achieves an overall accuracy of 75%. However, the model struggled to detect the 'Indeterminate' COVID-19 group, whose removal significantly improved the model's accuracy to 90%. The proposed explainable multi-classification DL model yields accurate detection of 'Classic', 'Probable', and 'Non-COVID' categories with poor detection ability for 'Indeterminate' COVID-19 cases. These findings are consistent with clinical studies that aimed at validating the BSTI reporting manually amongst consultant radiologists.
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Date
2025-02-26
Type
Article
Subject
Radiology
Citation
Fouad S, Usman M, Kabir R, Rajasekaran A, Morlese J, Nagori P, Bhatia B. Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study. J Imaging Inform Med. 2025 Feb 26. doi: 10.1007/s10278-025-01444-3. Epub ahead of print
Journal / Source Title
Journal of Imaging Informatics in Medicine
DOI
PMID
Publisher
Springer