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Many slice-level diagnosis methods17,26,27 have been proposed which had been quite equivalent to Li et al.’s operate. In this operate, we construct a clinically representative large-scale dataset with 11,356 CT scans from three centers in China and 4 publicly accessible databases, which is substantially bigger than prior research. Some AI systems employed 3D convolution neural networks, but only regarded as the reasonably easy two-category classification28,29. In addition, based on prediction score on just about every slice of CT volume, we find the lesion areas in COVID-19 individuals and execute a statistical study of various subsets of patients. To have an understanding of relative performances of CT and CXR for detecting COVID-19, we create both CT-based and CXR-based diagnosis systems, and test them using paired information, which has not been studied ahead of. We examine the diagnostic functionality of our CT-based diagnosis method with that of 5 radiologists in reader studies, and the final results show that the overall performance of this program is greater than that of seasoned radiologists. The precise phenotypic basis of the diagnosis output is also traced by an interpretation network, and radiomics evaluation is applied to comprehend the imaging traits of COVID-19. There are also a few COVID-19 detection systems applying CXR30, but the quantity of subjects with COVID-19 in these studies is significantly smaller than that in the research using CT, and no study has quantitively compared performances of CXR and CT making use of paired information.by