  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.
