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Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

Er-Yang Huan1, Gui-Hua Wen1, Shi-Jun Zhang2

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Computational and Mathematical Methods in Medicine
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Summary

This study introduces a deep convolutional neural network for body constitution classification using facial images, improving accuracy over traditional methods. The novel algorithm achieves 65.29% accuracy, offering a more efficient approach for traditional Chinese medicine constitution research.

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Area of Science:

  • Integrative Medicine
  • Artificial Intelligence in Healthcare
  • Traditional Chinese Medicine (TCM)

Background:

  • Body constitution classification is fundamental to Traditional Chinese Medicine (TCM) research.
  • Current identification methods, such as questionnaires, are inefficient and lack accuracy.
  • A need exists for objective and efficient methods to classify body constitutions.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for body constitution recognition based on facial images.
  • To improve the accuracy and efficiency of body constitution classification using deep learning techniques.
  • To provide an objective tool for TCM constitution research.

Main Methods:

  • A deep convolutional neural network (CNN) was employed for feature extraction from facial images.
  • Color features were combined with CNN-extracted features.
  • A Softmax classifier was utilized for final constitution type classification.
  • The algorithm was validated through comparative experiments.

Main Results:

  • The proposed deep convolutional neural network algorithm achieved an accuracy of 65.29% for body constitution classification.
  • The performance of the algorithm was deemed acceptable by traditional Chinese medicine practitioners.
  • The method demonstrated improved efficiency compared to traditional identification techniques.

Conclusions:

  • Deep convolutional neural networks offer a promising approach for objective body constitution classification using facial images.
  • The developed algorithm provides a more accurate and efficient alternative to traditional methods in TCM constitution research.
  • Facial image analysis combined with deep learning holds potential for advancing TCM diagnostics.