A Transfer Learning Based Approach for Detecting COVID-19 with Radiography Images

Abstract

In this study, few convolutional neural networks (CNN) have been trained with a transfer learning method to facilitate either binary classification of radiography images into COVID-19 infected and normal or ternary classification into normal, pneumonia, and COVID-19 infected. As the number of COVID-19 cases grow exponentially, the proposed solution can provide an early home based computer-aided diagnosis to ease the pressure on healthcare. The decision made by the model can advise a patient on whether it is critical to visit a doctor or not. In this paper, a CNN based transfer learning model was used to provide a superior precision in image classification. The neural network model was trained and tested using 1,183 radiography images to report the precision that can be attained in authentic conditions using three different CNNs. The accuracy of the model in classifying radiography images is 97.46% for ternary classification and 99.36% accuracy for binary classification using VGG-16 CNN architecture. In addition, the tested algorithm is also developed as a web application for detecting COVID-19 with Chest X-ray images and deployed in the cloud for public use.

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