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Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps

Author

Listed:
  • Yue Sun

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Songmin Dai

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Jide Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Yin Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Xiaoqiang Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China)

Abstract

The tooth-marked tongue is an important indicator in traditional Chinese medicinal diagnosis. However, the clinical competence of tongue diagnosis is determined by the experience and knowledge of the practitioners. Due to the characteristics of different tongues, having many variations such as different colors and shapes, tooth-marked tongue recognition is challenging. Most existing methods focus on partial concave features and use specific threshold values to classify the tooth-marked tongue. They lose the overall tongue information and lack the ability to be generalized and interpretable. In this paper, we try to solve these problems by proposing a visual explanation method which takes the entire tongue image as an input and uses a convolutional neural network to extract features (instead of setting a fixed threshold artificially) then classifies the tongue and produces a coarse localization map highlighting tooth-marked regions using Gradient-weighted Class Activation Mapping. Experimental results demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Yue Sun & Songmin Dai & Jide Li & Yin Zhang & Xiaoqiang Li, 2019. "Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:45-:d:206275
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    References listed on IDEAS

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