An Empirical Study on Detecting COVID-19 in Chest X-ray Images Using Deep Learning Based Methods

TL;DR

This paper investigates the use of deep learning convolutional neural networks to classify COVID-19 from chest X-ray images, offering a faster and more accurate alternative to traditional testing kits.

Contribution

It evaluates multiple CNN architectures for COVID-19 detection in X-ray images, demonstrating their effectiveness and efficiency compared to conventional testing methods.

Findings

CNNs achieved high accuracy in COVID-19 detection

Deep learning methods outperformed traditional testing in speed and precision

Different architectures showed varying levels of effectiveness

Abstract

Spreading of COVID-19 virus has increased the efforts to provide testing kits. Not only the preparation of these kits had been hard, rare, and expensive but also using them is another issue. Results have shown that these kits take some crucial time to recognize the virus, in addition to the fact that they encounter with 30% loss. In this paper, we have studied the usage of x-ray pictures which are ubiquitous, for the classification of COVID-19 chest Xray images, by the existing convolutional neural networks (CNNs). We intend to train chest x-rays of infected and not infected ones with different CNNs architectures including VGG19, Densnet-121, and Xception. Training these architectures resulted in different accuracies which were much faster and more precise than usual ways of testing.