ArticleDiagnostics (Basel, Switzerland)2022
An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Computerized tongue image analysis for non-invasive disease screening: a review.Chinese medicine · 2025Review
- Development of a machine learning model for hepatic steatosis screening using non-invasive Traditional Chinese Medicine diagnostics and clinical variables: a health checkup study with community screening potential.Frontiers in medicine · 2025Article
- Research on multi-label recognition of tongue features in stroke patients based on deep learning.Scientific reports · 2024Article
- Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network.BMC medical imaging · 2024Article
- Tongue feature dataset construction and real-time detection.PloS one · 2024Article
- Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence.Frontiers in medicine · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor-patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.
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