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Recommending prescription via tongue image to assist clinician

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Abstract

Traditional Chinese Medicine often use the prescription composed of herbs to cure the disease, which requires doctors with the rich professional knowledge and experience. It is much expected that the prescription can be generated automatically to assist doctors in prescribing using such as machine learning on the tongue images. However, it is confronted with two challenges. First, there is not a larger tongue image database available for machine learning. Second, there is no such machine learning method available for generating prescription according to the given tongue image. This paper begins with constructing a larger tongue image database, where each image corresponds to a prescription. It then uses auto-encoder to extract features for the tongue image, on which the recommendation neural network is proposed to recommend herbs for the prescription. Finally, a new prescription generation method is proposed to select optimal herbs from the recommended herbs to form the final prescription. Experimental results on our constructed databases validate the effectiveness and the superior performance of the proposed methods.

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Acknowledgments

This study was supported by China National Science Foundation (Grant Nos. 61273363 and 61976092 ), Guangdong Province Key Area R & D Plan Project (2020B1111120001), and Guangzhou Science and Technology Planning Project (Grant No. 201604020179 and 201803010088).

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Authors and Affiliations

  1. School of Computer Science and Engineering,, South China University of Technology, Guangzhou, China

    Guihua Wen, Kewen Wang & Yuhua Huang

  2. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China

    Huihui Li

  3. First Affiliated Hospital of Sun Yat sen University, Guangzhou, China

    Shijun Zhang

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  1. Guihua Wen
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Correspondence to Huihui Li.

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Wen, G., Wang, K., Li, H. et al. Recommending prescription via tongue image to assist clinician. Multimed Tools Appl 80, 14283–14304 (2021). https://doi.org/10.1007/s11042-020-10441-3

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