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

Abstract

In March 2020, the British Society of Thoracic Imaging (BSTI) introduced a reporting guidance for COVID-19 detection to streamline standardized reporting and enhance agreement between radiologist. 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 highlights the areas of the image most indicative of COVID-19 category. Our four-class classification DL framework achieves an overall accuracy and 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 finding are consistent with clinical studies which aimed at validating the BSTI reporting manually amongst consultant radiologists.