Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

快速诊断方法可以控制和预防像冠状病毒病2019（COVID-19）这样的大流行性疾病的传播，并帮助医生更好地管理高工作量条件下的患者。尽管实验室测试是当前的常规诊断工具，但它很耗时，成本高昂，并且需要配备完善的实验室进行分析。迄今为止，计算机断层扫描（CT）已成为诊断COVID-19患者的快速方法。但是，放射科医生在诊断COVID-19方面的表现中等。因此，需要进行其他调查以提高诊断COVID-19的性能。在这项研究中，提出了一种基于人工智能技术的快速有效的COVID-19诊断方法。包括来自108例经实验室验证的COVID-19（COVID-19组）和86例其他非典型和病毒性肺炎（非COVID-19组）患者的1020张CT切片。十个著名的卷积神经网络被用来区分COVID-19和非COVID-19组的感染：AlexNet，VGG-16，VGG-19，SqueezeNet，GoogleNet，MobileNet-V2，ResNet-18，ResNet-50， ResNet-101和Xception。在所有网络中，ResNet-101和Xception实现了最佳性能。ResNet-101可以将AUC为0.994（灵敏度为100％；特异性为99.02％；准确性为99.51％）将COVID-19与非COVID-19病例区分开。Xception的AUC为0.994（灵敏度为98.04％；特异性为100％；准确性为99.02％）。然而，放射科医生的表现中等，AUC为0.873（敏感性为89.21％；特异性为83.33％；精度为86.27％）。ResNet-101可以被视为表征和诊断COVID-19感染的高灵敏度模型，并且可以用作放射科的辅助工具。