Creating a Diagnostic Assistance System for Diseases in Kampo Medicine

Abstract

The aim of this study was to propose a method to assess images of the tongue captured using a polarized light camera for diagnostic use in Kampo medicine. Glossy and non-glossy images of the tongue were captured simultaneously using a polarizing camera and a polarizing plate. Data augmentation was performed by modulating the color and gloss, resulting in an increase in the number of images from 11 to 275. To create a data set, the values for which diseases were evaluated by Kampo doctors for all tongue images were taken as the correct values and combined with the features extracted from the tongue images. Using this data set, we constructed a diagnostic support module to evaluate diseases. The resulting mean absolute error of the assessment was 0.44 for qi deficiency, 0.42 for blood deficiency, 0.33 for blood stagnation, 0.36 for yin deficiency, and 0.55 for fluid stagnation, suggesting that the diagnostic assistance module was accurate, and our proposed learning and data augmentation methods were effective.

Keywords

• machine learning
• Kampo medicine
• tongue
• diagnostic assistance