Tongue Segmentation and Color Classification Using Deep Convolutional Neural Networks †

Bo Yan*, Sheng Zhang, Zijiang Yang, Hongyi Su, Hong Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Tongue color classification serves as important assistance for traditional Chinese medicine (TCM) doctors to make a precise diagnosis. This paper proposes a novel two-step framework based on deep learning to improve the performance of tongue color classification. First, a semantic-based CNN called SegTongue is applied to segment the tongues from the background. Based on DeepLabv3+, multiple atrous spatial pyramid pooling (ASPP) modules are added, and the number of iterations of fusions of low-level and high-level information is increased. After segmentation, various classical feature extraction networks are trained using softmax and center loss. The experiment results are evaluated using different measures, including overall accuracy, Kappa coefficient, individual sensitivity, etc. The results demonstrate that the proposed framework with SVM achieves up to 97.60% accuracy in the tongue image datasets.

Original languageEnglish
Article number4286
JournalMathematics
Volume10
Issue number22
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Convolutional Neural Networks (CNNs)
  • classification
  • feature extraction
  • prediction
  • semantic segmentation

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