Abstract
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.
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Acknowledgements
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.
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Xiao, M., Liu, G., Xia, Y., Xu, H. (2020). A Deep Learning Approach for Tongue Diagnosis. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_1
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DOI: https://doi.org/10.1007/978-3-030-14118-9_1
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