ArticleBMC medical imaging2024
Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From Image to Diagnosis: Convolutional Neural Networks in Tongue Lesions.Journal of imaging informatics in medicine · 2026Article
- Prior-guided feature fusion for tongue image-based gastrointestinal disease auxiliary diagnosis.Frontiers in physiology · 2026Article
- Development of a cold-heat syndrome classification model for children with allergic rhinitis based on multimodal data.Translational pediatrics · 2025Article
- YOLOv13-SwinTongue: Tongue Coating Diagnosis Using an Enhanced YOLOv13 with Swin Transformer.Sensors (Basel, Switzerland) · 2025Article
- Computerized tongue image analysis for non-invasive disease screening: a review.Chinese medicine · 2025Review
- Advanced deep feature engineering with crayfish optimization for diabetes detection using tongue images.Scientific reports · 2025Article
- Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.Scientific reports · 2025Article
- Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation.Chinese medicine · 2025Article
- Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.Journal of dentistry · 2025Article
- Artificial Intelligence in Oral Diagnosis: Detecting Coated Tongue with Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- Research on multi-label recognition of tongue features in stroke patients based on deep learning.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs).
methodsA dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance was evaluated on VGG19, ResNet50, ResNet101, and GoogLeNet networks using fusion based majority voting (FBMV) approach for the first time in the literature.
resultsIn the binary classification problem (normal vs. tongue lesion), the highest classification accuracy performance of 93,53% was achieved utilizing ResNet101, and this rate was increased to 95,15% with the application of the FBMV approach. In the five-class classification problem of tongue lesion types, the VGG19 network yielded the best accuracy rate of 83.93%, and the fusion approach improved this rate to 88.76%.
conclusionThe obtained test results showed that tongue lesions could be identified with a high accuracy by applying DCNNs. Further improvement of these results has the potential for the use of the proposed method in clinic applications.
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