ArticleBMC medical informatics and decision making2021
Tongue image quality assessment based on a deep convolutional neural network.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Pooled it
- Artificial intelligence and synthetic biology in traditional Chinese medicine: revolutionizing public health applications.Frontiers in plant science · 2026Review
- TongueNet-GYN: a multimodal deep learning framework for non-invasive gynecological disease screening in digital public health.Frontiers in public health · 2026Article
- Toward dynamic tongue diagnosis: a conceptual framework for temporal phenotyping.Frontiers in medicine · 2026Article
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Integration of tongue image features and tongue coating microbiome for differentiating dampness patterns in MASLD.Frontiers in endocrinology · 2026Article
- Computerized tongue image analysis for non-invasive disease screening: a review.Chinese medicine · 2025Review
- Clinical study of intelligent tongue diagnosis and oral microbiome for classifying TCM syndromes in MASLD.Chinese medicine · 2025Article
- Machine learning-assisted tongue image analysis for the diagnosis of Hashimoto's thyroiditis.Frontiers in medicine · 2025Article
- TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis.Frontiers in physiology · 2025Article
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- A practical guide to implementing artificial intelligence in traditional East Asian medicine research.Integrative medicine research · 2024Article
- Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study.Frontiers in psychiatry · 2024Article
- Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.Frontiers in cellular and infection microbiology · 2024Article
- Relationship chains of subhealth physical examination indicators: a cross-sectional study using the PLS-SEM approach.Scientific reports · 2023Article
- A lung cancer risk warning model based on tongue images.Frontiers in physiology · 2023Article
- Objective evaluation of tongue diagnosis ability using a tongue diagnosis e-learning/e-assessment system based on a standardized tongue image database.Frontiers in medical technology · 2023Article
- Deep learning model for tongue cancer diagnosis using endoscopic images.Scientific reports · 2022Article
- Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition.Frontiers in physiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM.
methodsMachine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score.
resultsThe experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA.
conclusionsOur research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.