Novel corona virus COVID‐19 has spread rapidly all over the world. Due to increasing COVID‐19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID‐19 virus. This work offers a decision support system based on the X‐ray image to diagnose the presence of the COVID‐19 virus. A deep learning‐based computer‐aided decision support system will be capable to differentiate between COVID‐19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID‐19 patients through chest radiography (or chest X‐ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view‐invariance and loss of information due to down‐sampling. In this paper, the capsule network (CapsNet)‐based system named visual geometry group capsule network (VGG‐CapsNet) for the diagnosis of COVID‐19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN‐based decision support system for the detection of COVID‐19. Through simulation results, it is found that VGG‐CapsNet has performed better than the CNN‐CapsNet model for the diagnosis of COVID‐19. The proposed VGG‐CapsNet‐based system has shown 97% accuracy for COVID‐19 versus non‐COVID‐19 classification, and 92% accuracy for COVID‐19 versus normal versus viral pneumonia classification. Proposed VGG‐CapsNet‐based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID‐19 virus in the human body through chest radiographic images.

中文翻译：

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使用放射线图像检测 COVID-19 的卷积胶囊网络

新型冠状病毒 COVID-19 已在全球迅速传播。由于 COVID-19 病例增加，检测试剂盒短缺。因此，迫切需要一种自动识别系统作为减少 COVID-19 病毒传播的解决方案。这项工作提供了一个基于 X 射线图像的决策支持系统来诊断 COVID-19 病毒的存在。基于深度学习的计算机辅助决策支持系统将能够区分 COVID-19 和肺炎。最近，卷积神经网络（CNN）被设计用于通过胸片（或胸部 X 光片）诊断 COVID-19 患者, CXR) 图像。然而，由于 CNN 的使用，这些决策支持系统存在一些限制。由于下采样，这些系统存在视图不变性和信息丢失的问题。在本文中，提出了基于胶囊网络（CapsNet）的系统，称为视觉几何组胶囊网络（VGG-CapsNet），用于诊断 COVID-19。由于胶囊网络 (CapsNet) 的使用，作者成功地消除了基于 CNN 的决策支持系统在检测 COVID-19 时发现的缺陷。通过仿真结果发现，对于 COVID-19 的诊断，VGG-CapsNet 的表现优于 CNN-CapsNet 模型。所提出的基于 VGG-CapsNet 的系统显示，COVID-19 与非 COVID-19 分类的准确度为 97%，COVID-19 与正常与病毒性肺炎分类的准确度为 92%。https://github.com/shamiktiwari/COVID19_Xray 上提出的基于 VGG-CapsNet 的系统可用于通过胸片图像检测人体内是否存在 COVID-19 病毒。