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