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Complexion Classification Based on Convolutional Neural Network

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DOI: 10.23977/jaip.2020.030105 | Downloads: 35 | Views: 1950

Author(s)

Yi Lin 1

Affiliation(s)

1 School of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210046, China

Corresponding Author

Yi Lin

ABSTRACT

Traditional Chinese medicine (TCM) has proved that the complexion of the human body is closely related to the health of each organ, and some visual features of the face can provide valuable clues for the diagnosis of diseases. This paper makes an attempt to develop an automated facial complexion classification model for objective TCM facial diagnosis based on convolutional neural network, and compared it with the existing and traditional machine learning facial classification methods, which has certain reference significance for the future development of deep learning algorithm in the field of TCM.

KEYWORDS

Inspection of TCM, Complexion recognition, Convolutional Neural Network, Classification

CITE THIS PAPER

Yi Lin. Complexion Classification Based on Convolutional Neural Network. Journal of Artificial Intelligence Practice (2020) Vol. 3: 22-30. DOI: http://dx.doi.org/10.23977/jaip.2020.030105.

REFERENCES

[1] A.P. Lu, H.W. Jia, C. Xiao, Q.P. Lu, Theory of traditional Chinese Medicine and therapeutic method of diseases, World J. Gastroenterol. 10 (2004) 1854–1856. https://doi.org/10.3748/wjg.v10.i13.1854.
[2] Z. Yan, K. Wang, N. Li, Computerized feature quantification of sublingual veins from color sublingual image, Comput. Methods Programs Biomed. 93 (2009) 192–205. https://doi.org/10.1016/j.cmpb.2008.09.006.
[3] L, Luo, C. Xue. Theories about inspection of heart disease in Huang Di Nei Jing and its use in practice, Chinese Achieves of Traditional Chinese Medicine. 11 (2011) 392-2394.
[4] K. Miyamoto, H. Takiwaki, G.G. Hillebrand, S. Arase, Measurement of Hyperpigmented Spots on the Face, (2002) 227–235.
[5] Z. Liang, J. Liu, A. Ou, H. Zhang, Z. Li, J.X. Huang, Deep generative learning for automated EHR diagnosis of traditional Chinese medicine, Comput. Methods Programs Biomed. 174 (2019) 17–23. https://doi.org/10.1016/j.cmpb. 2018.05.008.
[6] L. Wang, Y. Cai, Complexion basis function determine in TCM facial diagnosis, Measurement & Control Technology, 35 (2016) 129-133
[7] Y. Liu, P. Zhao, X. Lu, A segmentation algorithm for face consultation image of Traditional Chinese Medicine, Computer Knowledge and Technology, 13(2017) 183-185.
[8] H. Zhang, W. Ni, J. Li, Y. Jiang, K. Liu, Z. Ma, On standardization of basic datasets of electronic medical records in traditional Chinese medicine, Comput. Methods Programs Biomed. 174 (2019) 65–70. https://doi.org/10.1016/ j.cmpb. 2017.12.024.
[9] S. Lukman, Y. He, S.C. Hui, Computational methods for Traditional Chinese Medicine: A survey, Comput. Methods Programs Biomed. 88 (2007) 283–294. https://doi.org/10.1016/j.cmpb.2007.09.008.
[10] J. Das, H. Roy, Human face detection in color images using HSV color histogram and WLD, Proc. - 2014 6th Int. Conf. Comput. Intell. Commun. Networks, CICN 2014. (2014) 198–202. https://doi.org/10.1109/CICN.2014.54.
[11] R. Subban, R. Mishra, Face detection in color images based on explicitly-defined skin color model, Commun. Comput. Inf. Sci. 361 CCIS (2013) 570–582. https://doi.org/10.1007/978-3-642-36321-4-54.
[12] L. Zhuo, Y. Yang, J. Zhang, Y. Cao, Human facial complexion recognition of traditional Chinese medicine based on uniform color space, Int. J. Pattern Recognit. Artif. Intell. 28 (2014) 1450008. https://doi.org/10.1142/ S0218001414500086.
[13] F.F. Li, C. Zhao, Z. Xia, Y. Wang, X. Zhou, G.Z. Li, Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines, BMC Complement. Altern. Med. 12 (2012). https://doi.org/10.1186/1472-6882-12-127.
[14] Y. Yang, J. Zhang, L. Zhuo, Y. Cai, X. Zhang, Cheek region extraction method for face diagnosis of Traditional Chinese Medicine, Int. Conf. Signal Process. Proceedings, ICSP. 3 (2012) 1663–1667. https://doi.org/10.1109/ICoSP. 2012.6491900.
[15] K. Miyamoto, H. Takiwaki, G.G. Hillebrand, S. Arase et al., Development of a digital imaging system for objective measurement of hyperpigmented spots on the face, Skin Res Technol. 8(2002) 227-235. https://doi.org/10.1034/j.1600-0846.2002.00325.x
[16] L. Wang, Y. Cai, Complexion basis function determine in TCM facial diagnosis, Measurement & Control Technology, 35 (2016) 129-133.
[17] H. Li, B. Xu, N. Wang, J. Liu, Deep convolutional neural networks for classifying body constitution, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 9887 LNCS (2016) 128–135. https://doi.org/10.1007/978-3-319-44781-0_16.
[18] DAOSH. DS01-A [EB/OLT]. [2017-12-08]. http://www.daosh.com/en/product/det-ail.aspx?id=18.
[19] M.C. Hu, K.C. Lan, W.C. Fang, Y.C. Huang, T.J. Ho, C.P. Lin, M.H. Yeh, P. Raknim, Y.H. Lin, M.H. Cheng, Y.T. He, K.C. Tseng, Automated tongue diagnosis on the smartphone and its applications, Comput. Methods Programs Biomed. 174 (2019) 51–64. https://doi.org/10.1016/j.cmpb.2017.12.029.
[20] J. Yan, Y. Shen, Y. Wang, F. Li, C. Xia, R. Guo, C. Chen, Z. Gu, X. Shen, Nonlinear analysis of auscultation signals in Traditional Chinese Medicine using Wavelet Packet Transform and Approximate Entropy, Int. J. Funct. Inform. Personal. Med. 2 (2009) 325–340. https://doi.org/10.1504/IJFIPM.2009.030831.
[21] G.P. Liu, J.J. Yan, Y.Q. Wang, W. Zheng, T. Zhong, X. Lu, P. Qian, Deep learning based syndrome diagnosis of chronic gastritis, Comput. Math. Methods Med. 2014 (2014). https://doi.org/10.1155/2014/938350.
[22] Y. Wang, L. Ma, P. Liu, Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine, Comput. Methods Programs Biomed. 95 (2009) 249–257. https://doi.org/10.1016/j.cmpb.2009.03.004.

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