대한한의학회지 (The Journal of Korean Medicine)

  • 제44권4호
  • /
  • Pages.136-149
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  • 2023
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  • 1010-0695(pISSN)
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  • 2288-3339(eISSN)
대한한의학회 (The Society of Korean Medicine)
권호 표지
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Cold sensitivity classification using facial image based on convolutional neural network

  • lkoo Ahn (Korean Medicine Data Division, Korea Institute of Oriental Medicine ) ;
  • Younghwa Baek (Korean Medicine Data Division, Korea Institute of Oriental Medicine ) ;
  • Kwang-Ho Bae (Korean Medicine Data Division, Korea Institute of Oriental Medicine ) ;
  • Bok-Nam Seo (Clinical Medicine Division, Korea Institute of Oriental Medicine) ;
  • Kyoungsik Jung (Korean Medicine Data Division, Korea Institute of Oriental Medicine ) ;
  • Siwoo Lee (Korean Medicine Data Division, Korea Institute of Oriental Medicine )
  • 투고 : 2023.10.20
  • 심사 : 2023.08.26
  • 발행 : 2023.12.01
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초록

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

키워드

과제정보

This work was supported by the "Development of Korean Medicine Original Technology for Preventive Treatment Based on Integrative Big Data" grant from the Korea Institute of Oriental Medicine (KSN1731121).

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