The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.

2019 年 12 月爆发的新型冠状病毒病 (COVID-19) 已导致全球危机。该疾病于 2020 年 3 月 11 日被世界卫生组织 (WHO) 宣布为大流行病。目前，截至 2020 年 10 月 10 日，疫情已影响 200 多个国家，确诊病例超过 3700 万，死亡人数超过 100 万。转录聚合酶链反应（RT-PCR）是检测COVID-19疾病的标准方法，但存在假阳性、灵敏度低、成本高、需要专家进行检测等诸多挑战。随着病例数量的不断增长，迫切需要开发一种准确、快速、廉价的快速筛查方法。胸部 X 射线 (CXR) 扫描图像可被视为一种替代方法或确认方法，因为它们获取速度快且易于访问。尽管文献报道了许多对 CXR 图像进行分类和检测 COVID-19 感染的方法，但这些方法中的大多数只能识别两个类别（例如，COVID-19 与正常）。但是，需要能够对属于 COVID-19 类别本身的更广泛的 CXR 图像进行分类的完善模型，例如细菌性肺炎、非 COVID-19 病毒性肺炎和正常的 CXR 扫描。目前的工作建议使用基于预训练 AlexNet 模型的深度学习方法对从不同公共数据库获得的 COVID-19、非 COVID-19 病毒性肺炎、细菌性肺炎和正常 CXR 扫描进行分类。该模型经过训练以执行双向分类（即 COVID-19 与正常、细菌性肺炎与正常、非 COVID-19 病毒性肺炎与正常以及 COVID-19 与细菌性肺炎），三向分类分类（即 COVID-19 与细菌性肺炎与正常）和四向分类（即 COVID-19 与细菌性肺炎与非 COVID-19 病毒性肺炎与正常）。对于非 COVID-19 病毒性肺炎和正常（健康）CXR 图像，所提出的模型实现了 94.43% 的准确率、98.19% 的敏感性和 95.78% 的特异性。对于细菌性肺炎和正常的 CXR 图像，该模型实现了 91.43% 的准确率、91.94% 的灵敏度和 100% 的特异性。对于 COVID-19 肺炎和正常 CXR 图像，该模型实现了 99.16% 的准确度、97.44% 的灵敏度和 100% 的特异性。对于 COVID-19 肺炎和非 COVID-19 病毒性肺炎的分类 CXR 图像，该模型实现了 99.62% 的准确率、90.63% 的灵敏度和 99.89% 的特异性。对于三路分类，该模型达到了 94.00% 的准确率、91.30% 的灵敏度和 84.78% 的精度。最后，对于四维分类，模型达到了93.42%的准确率、89.18%的敏感性和98.92%的特异性。