Learning through One Shot: A Phase by Phase Approach for COVID-19 Chest X-ray Classification

Abstract

Today globally, coronavirus disease (COVID-19) has infected over more than 81 million people and killed at least 1771K. This is an infectious disease caused by a newly discovered coronavirus. As a result, scientists and researchers around the globe are now trying to find out the path to battle this disease in the most effective way. Chest X-rays are a widely available modality for immediate care in diagnosing COVID-19. Detection and diagnosis of COVID-19 chest X-rays would be more precise for the current situation. In this paper, a phase by phase approach using the concept of one shot learning is introduced for effective classification of chest X-ray images. The proposed method utilizes the application of Entropy for selecting best describing images for effective learning purposes. The proposed model is evaluated on a publically available large dataset of size 24614 images comprising of three classes viz COVID-19, Normal and Non-COVID. The obtained results are promising and encouraging.