Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

TLDR

The COVID‑19 pandemic continues to devastate global health, and chest radiography is a key screening tool for detecting infected patients. This study aims to automatically detect COVID‑19 pneumonia from digital chest X‑ray images using deep convolutional neural networks to maximize diagnostic accuracy. Using a dataset of 864 COVID‑19, 1,345 viral pneumonia, and 1,341 normal images, the authors applied Inception V3 with transfer learning, achieving over 98 % classification accuracy. The results show that transfer learning provides robust, high‑performance detection that is easily deployable for COVID‑19 screening.

Abstract

Abstract The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.