An Empirical Study on Detecting COVID-19 in Chest X-ray Images Using Deep Learning Based Methods
This addresses the need for rapid and accurate COVID-19 diagnosis, but it is incremental as it applies existing CNN architectures to a new dataset.
The study tackled the problem of slow and inaccurate COVID-19 testing by using deep learning to classify chest X-ray images, achieving faster and more precise results than traditional methods.
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.