The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID-19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients.

新型冠状病毒（COVID-19）可以说是21世纪人类最大的挑战。该疾病的发展和传播在所有国家都增加了死亡率。因此，对COVID-19进行快速诊断对于治疗和控制该疾病是必要的。本文提出了一种使用拟议的深度神经网络自动识别肺炎（包括COVID-19）的新方法。在建议的方法中，根据健康，病毒，细菌和COVID-19类别，使用胸部X射线图像将7种不同功能情景中的2-4类分开。在提出的体系结构中，将生成对抗网络（GAN）与深度迁移学习和LSTM网络的融合一起使用，而无需进行针对肺炎分类的特征提取/选择。除一种情况外，我们在所有情况下均达到了90％以上的准确度，并且从健康组中分离出COVID-19的准确度也达到了99％。我们还将深层建议的网络与最近在肺炎检测研究中广泛使用的其他深层转移学习网络（包括Inception-ResNet V2，Inception V4，VGG16和MobileNet）进行了比较。与其他深度转移学习方法相比，基于拟议网络的结果在准确性，精确度，敏感性和特异性方面非常有希望。根据所提出方法的高性能，可以在患者治疗期间使用它。我们还将深层建议的网络与最近在肺炎检测研究中广泛使用的其他深层转移学习网络（包括Inception-ResNet V2，Inception V4，VGG16和MobileNet）进行了比较。与其他深度转移学习方法相比，基于拟议网络的结果在准确性，精确度，敏感性和特异性方面非常有希望。根据所提出方法的高性能，可以在患者治疗期间使用它。我们还将深层建议的网络与最近在肺炎检测研究中广泛使用的其他深层转移学习网络（包括Inception-ResNet V2，Inception V4，VGG16和MobileNet）进行了比较。与其他深度转移学习方法相比，基于拟议网络的结果在准确性，精确度，敏感性和特异性方面非常有希望。根据所提出方法的高性能，可以在患者治疗期间使用它。