  To develop a deep-learning model that integrates radiomics analysis for
enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using
chest X-ray image, two deep-learning models were trained based on a pre-trained
VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the
2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the
saliency map analysis of the 1st model were stacked as the input. Both models
were developed using 812 chest X-ray images with 262/288/262
COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned
as training-validation/independent test sets. In 1st model using X-ray as the
sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC
Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1)
0.90$\pm$0.07 vs 0.78$\pm$0.09, 2) 0.94$\pm$0.04 vs 0.94$\pm$0.04, 3)
0.93$\pm$0.03 vs 0.89$\pm$0.03, and 4) 0.96$\pm$0.02 vs 0.92$\pm$0.04. In the
2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their
highest cross-correlations with the saliency maps of the 1st model. The
corresponding results demonstrated significant improvements (p<0.05) of
COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95$\pm$0.04 vs
0.85$\pm$0.04, 2) 0.97$\pm$0.02 vs 0.96$\pm$0.02, 3) 0.97$\pm$0.02 vs
0.93$\pm$0.02, and 4) 0.99$\pm$0.01 vs 0.97$\pm$0.02. The reduced variations
suggested a superior robustness of 2nd model design.
