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Tongue Segmentation and Color Classification Using Deep Convolutional Neural Networks

Author

Listed:
  • Bo Yan

    (School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

  • Sheng Zhang

    (School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

  • Zijiang Yang

    (School of Information Technology, York University, Toronto, ON M3J 1P3, Canada)

  • Hongyi Su

    (School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

  • Hong Zheng

    (School of Computer Science, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Tongue color classification serves as important assistance for traditional Chinese medicine (TCM) doctors to make a precise diagnosis. This paper proposes a novel two-step framework based on deep learning to improve the performance of tongue color classification. First, a semantic-based CNN called SegTongue is applied to segment the tongues from the background. Based on DeepLabv3+, multiple atrous spatial pyramid pooling (ASPP) modules are added, and the number of iterations of fusions of low-level and high-level information is increased. After segmentation, various classical feature extraction networks are trained using softmax and center loss. The experiment results are evaluated using different measures, including overall accuracy, Kappa coefficient, individual sensitivity, etc. The results demonstrate that the proposed framework with SVM achieves up to 97.60% accuracy in the tongue image datasets.

Suggested Citation

  • Bo Yan & Sheng Zhang & Zijiang Yang & Hongyi Su & Hong Zheng, 2022. "Tongue Segmentation and Color Classification Using Deep Convolutional Neural Networks," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4286-:d:974268
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    Cited by:

    1. Yunlong Ding & Di-Rong Chen, 2023. "Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks," Mathematics, MDPI, vol. 11(15), pages 1-13, July.

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