A Deep Learning Approach for Tongue Diagnosis

  • Meng Xiao
  • Guozheng Liu
  • Yu Xia
  • Hao XuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


With the improvement of living standards, people are paying more attention to healthcare, but there is still a long way to go to improve healthcare. A usable, intelligent aided diagnosis measure can be helpful for people to achieve daily health management. Several studies suggested that tongue features can directly reflect a person’s physical state. In this paper, we apply tongue diagnosis to daily health management. To this end, this paper proposes and implements a classification model of tongue image syndromes based on convolutional neural network and carries out an experiment to verify the feasibility and stability of the model. Finally, a tongue diagnosis platform that can be used for daily health management is implemented. In the two-class experiment, our model has achieved a good result. In addition, our model performs better on classifying the tongue image syndrome compared with traditional machine learning methods.


Healthcare Health management Tongue image diagnosis Convolutional neural network 



This research was funded by the [Development Project of Jilin Province of China] grant number [20160414009GH, 20170101006JC, 20160204022GX], the [National Natural Science Foundation of China] grant number [61472159, 71620107001, 71232011], the [Jilin Provincial Key Laboratory of Big Date Intelligent Computing] grant number [20180622002JC]. The Premier-Discipline Enhancement Scheme was supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme was supported by Guangdong Government Funds.


  1. 1.
    Berry, L., Bendapudi, N.: Health care: a fertile field for service research. J. Serv. Res. 10(2), 111–122 (2007)CrossRefGoogle Scholar
  2. 2.
    Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. (2017)Google Scholar
  3. 3.
    Compston, H.: King Trends and the Future of Public Policy. Palgrave Macmillan, Basingstoke (2006)CrossRefGoogle Scholar
  4. 4.
    Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A.: A geometric snake model for segmentation of medical imagery. IEEE Trans. Med. Imaging 16(2), 199–209 (1997)CrossRefGoogle Scholar
  5. 5.
    Tomczyk, A., Szczepaniak, P., Pryczek, M.: Cognitive hierarchical active partitions in distributed analysis of medical images. J. Amb. Intel. Hum. Comp. 4(3), 357–367 (2012)CrossRefGoogle Scholar
  6. 6.
    Quist, M.J.: A Method of Image Registration and Medical Image Data Processing Apparatus (2017)Google Scholar
  7. 7.
    Zhi, L., Zhang, D., Yan, J., Li, Q., Tang, Q.: Classification of hyperspectral medical tongue images for tongue diagnosis. Comput. Med. Imag. Grap. 31(8), 672–678 (2007)CrossRefGoogle Scholar
  8. 8. Keep (2018). Accessed 21 Aug 2018
  9. 9. Lifesum (2018). Accessed 21 Aug 2018
  10. 10.
    Zhang, B., Wang, X., You, J., Zhang, D.: Tongue color analysis for medical application. Evid. Based Complementary Altern. Med. 2013, 1–11 (2013)Google Scholar
  11. 11.
    Chiu, C.C., Lin, H.S., Lin, S.L.: A structural texture recognition approach for medical diagnosis through tongue. Biomed. Eng. Appl. Basis Commun. 7(2), 143–148 (1995)Google Scholar
  12. 12.
    Wang, Y., Yang, J., Zhou, Y., Wang, Y.: Region partition and feature matching based color recognition of tongue image. Pattern Recogn. Lett. 28(1), 11–19 (2007)CrossRefGoogle Scholar
  13. 13.
    Li, C., Yuen, P.: Tongue image matching using color content. Pattern Recogn. 35(2), 407–419 (2002)CrossRefGoogle Scholar
  14. 14.
    Pang, B., Zhang, D., Wang, K.: The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine. IEEE Trans. Med. Imaging 24(8), 946–956 (2005)CrossRefGoogle Scholar
  15. 15.
    Liu, Z., Yan, J., Zhang, D., Li, Q.: Automated tongue segmentation in hyperspectral images for medicine. Appl. Opt. 46(34), 8328 (2007)CrossRefGoogle Scholar
  16. 16.
    Zhang, D., Liu, Z., Yan, J.: Dynamic tongueprint: a novel biometric identifier. Pattern Recogn. 43(3), 1071–1082 (2010)CrossRefGoogle Scholar
  17. 17.
    Obafemiajayi, T., Kanawong, R., Xu, D., Duan, Y.: Features for automated tongue image shape classification. In: IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 273–279 (2013)Google Scholar
  18. 18.
    Chiu, C.: A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue. Comput. Methods Programs Biomed. 61(2), 77–89 (2000)CrossRefGoogle Scholar
  19. 19.
    Ma, C., Sun, C., Song, D., Li, X., Xu, H.: A deep learning approach for online learning emotion recognition. In: 13th International Conference on Computer Science & Education, pp. 1–5 (2018)Google Scholar
  20. 20.
    Hou, J., Su, H.Y., Yan, B., Zheng, H., Sun, Z.L., Cai, X.C.: Classification of tongue color based on CNN. In: IEEE International Conference on Big Data Analysis, pp. 725–729 (2017)Google Scholar
  21. 21.
    Hu, Y., Wen, G., Liao, H., Wang, C., Dai, D., Yu, Z., Zhang, J.: Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics (2018)Google Scholar
  22. 22.
    Meng, D., Cao, G., Duan, Y., Zhu, M., Tu, L., Xu, J., Xu, D.: A deep tongue image features analysis model for medical application. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1918–1922 (2017)Google Scholar
  23. 23.
    Kanawong, R., Obafemi-Ajayi, T., Ma, T., Xu, D., Li, S., Duan, Y.: Automated tongue feature extraction for ZHENG classification in traditional Chinese medicine. Evid. Based Complementary Altern. Med. 2012, 1–14 (2012)CrossRefGoogle Scholar
  24. 24.
    Obafemi-Ajayi, T., Xu, D., Yu, J., Duan, Y., Kanawong, R., Li, S.: ZHENG classification in Traditional Chinese Medicine based on modified specular-free tongue images. In: IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 288–294 (2013)Google Scholar

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Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.School of ManagementJilin UniversityChangchunChina
  3. 3.Department of Computer Science and TechnologyZhuhai College of Jilin UniversityZhuhaiChina

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