At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection.

当前，新冠肺炎疫情全球大流行，形势依然严峻。由于最近爆发的新型冠状病毒肺炎，可用于深度学习分析的新型胸部 X 光（CXR）图像非常罕见。为了解决这个问题，我们提出了一个集成了卷积神经网络和胶囊网络的深度学习框架。DenseCapsNet是一种新的深度学习框架，由密集卷积网络（DenseNet）和胶囊神经网络（CapsNet）融合而成，利用各自的优势，减少卷积神经网络对大量数据的依赖。使用健康患者以及其他肺炎和新型冠状病毒肺炎患者肺部的 750 张 CXR 图像，该方法可以获得 90 的准确度。7%，F1分数90.9%，检测COVID-19的灵敏度可达96%。这些结果表明，深度融合神经网络 DenseCapsNet 在新型冠状病毒肺炎 CXR 射线照相检测中具有良好的性能。