Lung Segmentation Enhances COVID-19 Detection

Abstract

Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It results in a macro F1 score of 84.92% for predicting the presence of COVID-19 in the CT scans on the test dataset which came second place in the competition. The model achieved a macro F1 score of 59.06% on the test dataset for predicting the severity of COVID-19 which was the best performing model for that task of the competition.