论文标题
基于X Ception和Resnet50v2的串联，用于从胸部X射线图像中检测Covid-19和肺炎的改良深卷积神经网络
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
论文作者
论文摘要
在本文中，我们培训了几个深度卷积网络，并基于两个开源数据集将X射线图像分类为三个类别：正常，肺炎和Covid-19。我们的数据包含180张X射线图像，这些图像属于感染Covid-19的人，我们试图应用方法以获得最佳的结果。在这项研究中，我们介绍了一些培训技术，可以帮助网络学习不平衡的数据集（COVID-19的案例较少以及其他类别的案例）。我们还提出了一个神经网络，该网络是xception和resnet50v2网络的串联。该网络通过利用两个强大的网络提取的多个功能来实现最佳精度。为了评估我们的网络，我们已经对11302张图像进行了测试，以报告可在实际情况下实现的实际准确性。提出的检测COVID-19病例的网络的平均准确性为99.50％，所有类别的总体平均准确性为91.4％。
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%.
