An Effectiveness Verification for Evaluating the Amount of WTCI Tongue Coating Using Deep Learning

딥러닝을 이용한 WTCI 설태량 평가를 위한 유효성 검증

  • Lee, Woo-Beom (Department of Information Communication Engineering, Sangji University)
  • 이우범 (상지대학교 정보통신공학과)
  • Received : 2019.12.11
  • Accepted : 2019.12.26
  • Published : 2019.12.31


A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.


Supported by : 상지대학교


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