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Tongue image quality assessment based on a deep convolutional neural network

  • Jiang, Tao1
  • Hu, Xiao-juan2
  • Yao, Xing-hua1
  • Tu, Li-ping1
  • Huang, Jing-bin1
  • Ma, Xu-xiang1
  • Cui, Ji1
  • Wu, Qing-feng3
  • Xu, Jia-tuo1
  • 1 Basic Medical College Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China , Shanghai (China)
  • 2 Shanghai University of TCM, 1200 Cailun Road, Shanghai, 201203, China , Shanghai (China)
  • 3 Xiamen University, Xiamen, 361005, China , Xiamen (China)
Published Article
BMC Medical Informatics and Decision Making
Springer (Biomed Central Ltd.)
Publication Date
May 05, 2021
DOI: 10.1186/s12911-021-01508-8
Springer Nature


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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