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ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1559389
This article is part of the Research Topic Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume II View all 10 articles
Deep learning-based automated tongue analysis system for assisted Chinese medicine diagnosis
Provisionally accepted- 1 Sichuan University, Chengdu, China
- 2 Sichuan Second Hospital of TCM, Sichuan, China
This study proposes an automated tongue analysis system that combines deep learning with traditional Chinese medicine to enhance the accuracy and objectivity of tongue diagnosis. The system includes a hardware device to provide a stable acquisition environment, an improved semi-supervised learning segmentation algorithm based on U2net, a high-performance colour correction module for standardising the segmented images, and a tongue image analysis algorithm that fuses different features according to the characteristics of each feature of the TCM tongue image.Experimental results demonstrate the system's performance and robustness in feature extraction and classification. The proposed methods ensure consistency and reliability in tongue analysis, addressing key challenges in traditional practices and providing a foundation for future correlation studies with endoscopic findings.
Keywords: Tongue images, image segmentation, image classification, Tongue diagnosis, neural networks, color correction
Received: 12 Jan 2025; Accepted: 27 Feb 2025.
Copyright: © 2025 Chen, Chen, Zhou, Zhu, He and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jing Zhang, Sichuan University, Chengdu, China
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