Automated COVID-19 Detection and Diagnosis Framework Based on Severity Assessment

Abstract

Abstract Computed tomography (CT) has been an important complementary indicator in the diagnosis of coronavirus disease 2019 (COVID-19). The pandemic of COVID-19 has led to a sharp increase in the number of suspected cases, which puts great pressure on radiologists. A computer-supported assisting methodology is essential to get the preliminary diagnosis regarding the pneumonia infection. In this paper, we proposed a deep learning framework for COVID-19 diagnosis and severity assessment using chest CT. The framework can not only distinguish COVID-19 patients from healthy people, but also assess the severity of patients as early or progressive stage, which makes patients with different conditions in baseline test get reasonable allocation of medical resources. The framework is composed of two modules: segmentation module and diagnosis module. Segmentation module is designed to extract the regions of interest and calculate the opacity percentage, while diagnosis module is utilized to identify suspect cases and divide them into three categories: health, early stage, and progressive stage. A total of 150 CT exams were used to train and test. An F1 score of 95.44% for COVID-19 detection and an F1 score of 90.87% for severity assessment are obtained. We also evaluated the influence of the opacity percentage calculated by the segmentation module on the classification results. By using the opacity percentage characteristic, the accuracy is improved from 94.16% to 97.42%.

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