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Purpose: This study aimed to develop and validate computer-aided diagnosis(CXDx) system for classification between COVID-19 pneumonia, non-COVID-19pneumonia, and the healthy on chest X-ray (CXR) images.  Materials and Methods: From two public datasets, 1248 CXR images wereobtained, which included 215, 533, and 500 CXR images of COVID-19 pneumoniapatients, non-COVID-19 pneumonia patients, and the healthy samples. Theproposed CADx system utilized VGG16 as a pre-trained model and combination ofconventional method and mixup as data augmentation methods. Other types ofpre-trained models were compared with the VGG16-based model. Single type or nodata augmentation methods were also evaluated. Splitting oftraining/validation/test sets was used when building and evaluating the CADxsystem. Three-category accuracy was evaluated for test set with 125 CXR images.  Results: The three-category accuracy of the CAD system was 83.6% betweenCOVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity forCOVID-19 pneumonia was more than 90%. The combination of conventional methodand mixup was more useful than single type or no data augmentation method.  Conclusion: This study was able to create an accurate CADx system for the3-category classification. Source code of our CADx system is available as opensource for COVID-19 research.

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Automatic classification between COVID-19 pneumonia, non-COVID-19  pneumonia, and the healthy on chest X-ray image: combination of data  augmentation methods