Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks
Asif, S.; Wenhui, Y.; Jin, H.; Tao, Y.; Jinhai, S.
Show 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.
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