COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.

COVID-19 大流行对世界科学提出了挑战。国际社会试图尽快寻找、应用或设计新的方法来诊断和治疗COVID-19患者。目前，诊断感染患者的可靠方法是逆转录聚合酶链反应。该方法既昂贵又耗时。因此，设计新颖的方法很重要。在本文中，我们使用了三种基于深度学习的方法，通过肺部 X 射线图像来检测和诊断 COVID-19 患者。对于疾病的诊断，我们提出了两种算法，包括基于图像分形特征的深度神经网络（DNN）和直接使用肺部图像的卷积神经网络（CNN）方法。结果分类表明，所提出的 CNN 架构具有更高的准确度 (93.2%) 和灵敏度 (96.1%)，优于准确度为 83.4% 和灵敏度为 86% 的 DNN 方法。在分割过程中，我们提出了一种 CNN 架构来查找肺部图像中的感染组织。结果表明，该方法几乎可以检测到感染区域，准确率高达 83.84%。这一发现还可用于监测和控制受感染区域患者的生长。