Pneumonia is a global disease that causes high children mortality. The situation has even been worsening by the outbreak of the new coronavirus named COVID-19, which has killed more than 983,907 so far. People infected by the virus would show symptoms like fever and coughing as well as pneumonia as the infection progresses. Timely detection is a public consensus achieved that would benefit possible treatments and therefore contain the spread of COVID-19. X-ray, an expedient imaging technique, has been widely used for the detection of pneumonia caused by COVID-19 and some other virus. To facilitate the process of diagnosis of pneumonia, we developed a deep learning framework for a binary classification task that classifies chest X-ray images into normal and pneumonia based on our proposed CGNet. In our CGNet, there are three components including feature extraction, graph-based feature reconstruction and classification. We first use the transfer learning technique to train the state-of-the-art convolutional neural networks (CNNs) for binary classification while the trained CNNs are used to produce features for the following two components. Then, by deploying graph-based feature reconstruction, we, therefore, combine features through the graph to reconstruct features. Finally, a shallow neural network named GNet, a one layer graph neural network, which takes the combined features as the input, classifies chest X-ray images into normal and pneumonia. Our model achieved the best accuracy at 0.9872, sensitivity at 1 and specificity at 0.9795 on a public pneumonia dataset that includes 5,856 chest X-ray images. To evaluate the performance of our proposed method on detection of pneumonia caused by COVID-19, we also tested the proposed method on a public COVID-19 CT dataset, where we achieved the highest performance at the accuracy of 0.99, specificity at 1 and sensitivity at 0.98, respectively.

肺炎是一种导致儿童死亡率很高的全球性疾病。名为 COVID-19 的新型冠状病毒的爆发甚至使情况进一步恶化，迄今为止已造成超过 983,907 人死亡。随着感染的进展，感染该病毒的人会出现发烧、咳嗽和肺炎等症状。及时检测是公众达成的共识，这将有利于可能的治疗，从而遏制 COVID-19 的传播。X 射线是一种便捷的成像技术，已广泛用于检测由 COVID-19 和其他一些病毒引起的肺炎。为了促进肺炎的诊断过程，我们开发了一个用于二元分类任务的深度学习框架，该框架基于我们提出的 CGNet 将胸部 X 射线图像分类为正常和肺炎。在我们的 CGNet 中，由三个部分组成，包括特征提取、基于图的特征重建和分类。我们首先使用迁移学习技术来训练最先进的卷积神经网络（CNN）进行二元分类，同时训练后的 CNN 用于为以下两个组件生成特征。然后，通过部署基于图的特征重建，我们通过图组合特征来重建特征。最后，一个名为 GNet 的浅层神经网络（一种单层图神经网络）以组合特征作为输入，将胸部 X 射线图像分类为正常和肺炎。我们的模型在包含 5,856 张胸部 X 射线图像的公共肺炎数据集上实现了 0.9872 的最佳准确度、1 的灵敏度和 0.9795 的特异性。为了评估我们提出的方法在检测 COVID-19 引起的肺炎方面的性能，我们还在公共 COVID-19 CT 数据集上测试了该方法，其中我们以 0.99 的准确度、1 的特异性和 1 的灵敏度实现了最高性能分别为 0.98。