Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.

冠状病毒是一种传播速度非常快的流行病。因此，它在全世界许多地区产生了极具破坏性的影响。尽快检测出 COVID-19 疾病以抑制疾病的传播至关重要。 COVID-19 疾病与其他肺部感染的相似性使得诊断变得困难。此外，COVID-19 的高传播率增加了对快速病例诊断系统的需求。为此，人们对各种计算机辅助（如CNN、DNN等）深度学习模型的兴趣有所增加。在这些模型中，主要应用放射学图像来确定阳性病例。最近的研究表明，放射图像包含检测冠状病毒的重要信息。在这项研究中，提出了一种新颖的人工神经网络，即卷积 CapsNet，通过使用带有胶囊网络的胸部 X 射线图像来检测 COVID-19 疾病。所提出的方法旨在通过二元分类（COVID-19 和无发现）和多类分类（COVID-19 和无发现以及肺炎）为 COVID-19 疾病提供快速、准确的诊断。该方法对于二元类和多类分别实现了 97.24% 和 84.22% 的准确率。据认为，所提出的方法可以帮助医生诊断 COVID-19 疾病并提高诊断性能。此外，我们认为所提出的方法可能是通过提供快速筛查来诊断 COVID-19 的替代方法。