Automated detection of COVID-19 on a small dataset of chest CT images using metric learning

Madan, S.; Chaudhury, S.; Gandhi, T. K.

Coronavirus disease has caused unprecedented chaos across the globe causing potentially fatal pneumonia, since the beginning of 2020. Researchers from different communities are working in conjunction with front-line doctors and policymakers to better understand the disease. The key to prevent the spread is a rapid diagnosis, prioritized isolation, and fastidious contact tracing. Recent studies have confirmed the presence of underlying patterns on chest CT for patients with COVID-19. We present a completely automated framework to detect COVID-19 using chest CT scans, only needing a small number of training samples. We present a few-shot learning technique based on the Triplet network in comparison to the conventional deep learning techniques which require a substantial amount of training examples. We used 140 chest CT images for training and the rest for testing from a total of 2482 images for both COVID-19 and non-COVID-19 cases from a publicly available dataset. The model trained with chest CT images achieves an AUC of 0.94, separates the two classes into distinct clusters;thereby giving correct prediction accuracy on the evaluation dataset. © 2021 IEEE.