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The worldwide spread of coronavirus disease (COVID-19) has become athreatening risk for global public health. It is of great importance to rapidlyand accurately screen patients with COVID-19 from community acquired pneumonia(CAP). In this study, a total of 1658 patients with COVID-19 and 1027 patientsof CAP underwent thin-section CT. All images were preprocessed to obtain thesegmentations of both infections and lung fields, which were used to extractlocation-specific features. An infection Size Aware Random Forest method(iSARF) was proposed, in which subjects were automated categorized into groupswith different ranges of infected lesion sizes, followed by random forests ineach group for classification. Experimental results show that the proposedmethod yielded sensitivity of 0.907, specificity of 0.833, and accuracy of0.879 under five-fold cross-validation. Large performance margins againstcomparison methods were achieved especially for the cases with infection sizein the medium range, from 0.01% to 10%. The further inclusion of Radiomicsfeatures show slightly improvement. It is anticipated that our proposedframework could assist clinical decision making.

Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification