Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지

An Effectiveness Verification for Evaluating the Amount of WTCI Tongue Coating Using Deep Learning

딥러닝을 이용한 WTCI 설태량 평가를 위한 유효성 검증

  • Lee, Woo-Beom (Department of Information Communication Engineering, Sangji University)
  • 이우범 (상지대학교 정보통신공학과)
  • Received : 2019.12.11
  • Accepted : 2019.12.26
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

한방 설진에서 WTCI(Winkel Tongue Coating Index) 설태 평가는 환자의 설태량 측정을 위한 중요한 객관적인 지표 중의 하나이다. 그러나 이전의 WTCI 설태 평가는 혀영상으로부터 설태 부분을 추출하여 전체 혀 영역에서 추출된 설태 영역의 비율을 정량적으로 측정하는 방법이 대부분으로 혀영상의 촬영 조건이나 설태 인식 성능에 의해서 비객관적 측정의 문제점이 있었다. 따라서 본 논문에서는 빅데이터를 기반으로 하는 인공지능의 딥러닝 방법을 적용하여 설태량을 분류하여 평가하는 딥러닝 기반의 WTCI 평가 방법을 제안하고 검증한다. 설태 평가 방법에 있어서 딥러닝의 유효성 검증을 위해서는 CNN을 학습 모델로 사용하여 소태, 박태, 후태의 3가지 유형의 설태량을 분류한다. 설태 샘플 영상을 학습 및 검증 데이터로 구축하여 CNN 기반의 딥러닝 모델로 학습한 결과 96.7%의 설태량 분류 정확성을 보였다.

Keywords

References

  1. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11):2278- 2324, 1998. https://doi.org/10.1109/5.726791
  2. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks.", advances in neural information processing systems, pp.1097-1105. 2012.
  3. M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional neural networks", European conference on computer vision. Springer, Cham, pp.818-833, 2014
  4. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", In ICLR, 2015.
  5. Christian Szegedy, et al., "Going Deeper With Convolutions", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
  6. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778 , 2016.
  7. W. B. Lee, "WTCI Tongue Coating Evaluation by analyzing a Ultraviolet Rays Tongue Image Channel", Journal of The Institute of Signal Processing and Systems, vol. 16(3), pp.96-101, 2015.
  8. W. B. Lee, "Implementation of Computerized Assistant Diagnosis Software for Tongue Diagnosis in the Oriental Medicinel", Journal of The Institute of Electronics and Information Engineers, vol. 51(6), pp.175-182, 2014. https://doi.org/10.5573/ieie.2014.51.6.175
  9. E. G. Winkel, et al., "Clinical effects of a new mouthrinse containing chlorhexidine, cetylpyridinium chloride and zinc-lactate on oral halitosis", Journal of Clinical Periodontology, vol.30(4), pp.300-306, 2003. https://doi.org/10.1034/j.1600-051X.2003.00342.x
  10. Deep Learning Made Easy with Deep Cognition, www.kdnuggets.com, 2018.
  11. Deep Learning Studio Solutions(Exxact), www.exxactcorp.com, 2018.