一种基于Xception和ResNet50V2的改进的深度卷积神经网络，用于从胸部X射线图像中检测COVID-19和肺炎

在本文中，我们基于两个开放源数据集，对几种深层卷积网络进行了训练，并引入了将X射线图像分为三类的训练技术：正常，肺炎和COVID-19。我们的数据包含属于COVID-19感染者的180幅X射线图像，我们尝试采用各种方法以获得最佳效果。..

A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2

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%.