  In this paper, we have trained several deep convolutional networks with
introduced training techniques for classifying X-ray images into three classes:
normal, pneumonia, and COVID-19, based on two open-source datasets. Our data
contains 180 X-ray images that belong to persons infected with COVID-19, and we
attempted to apply methods to achieve the best possible results. In this
research, we introduce some training techniques that help the network learn
better when we have an unbalanced dataset (fewer cases of COVID-19 along with
more cases from other classes). We also propose a neural network that is a
concatenation of the Xception and ResNet50V2 networks. This network achieved
the best accuracy by utilizing multiple features extracted by two robust
networks. For evaluating our network, we have tested it on 11302 images to
report the actual accuracy achievable in real circumstances. The average
accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and
the overall average accuracy for all classes is 91.4%.
