Apply Deep Learning on Chest X-Rays Images for COVID-19 Disease Detection by Using Transfer Learning

Nguyen Minh Thien, Long Ton That, Kien Trang, An Hoang Nguyen, Bao Quoc Vuong

Abstract: The outbreak of Coronavirus has caused a million fatal cases recorded globally. The challenge in dealing with the SARS-CoV-2 virus is due to its patients carrying similar symptoms with common viral pneumonia. Therefore, it is essential for doctors to recognize and differentiate the infected patients of this virus in early diagnostic steps, such as using Chest X-Ray images. For that purpose, applying transfer learning with pre-trained models is considered in this work, with the aim to single out the Corona infected images from healthy lungs or other common viral pneumonia. The Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays) has been applied to train and evaluate the performance of the implemented models. The dataset consists of 4 classes with a total number of thousands of images, being Normal, COVID-19, Viral - Pneumonia, and Bacterial - Pneumonia, respectively. The high accuracy recorded results from the dataset help to nominate the suitable models for early recognition of Corona infected patients, which allows early intervention and the possibility of being completely cured of the deadly virus.