Abstract
The color of the tongue is closely related to the patient’s physical condition and pathological condition, and it plays a very important position in the treatment and diagnosis of traditional Chinese medicine (TCM) clinical medicine. Convolutional neural network (CNN) has achieved fruitful results in image classification. Therefore, the method combining CNN with tongue color classification is proposed. Frist, initial data set is obtained by standardizing tongue image acquisition and tongue image preprocessing. Then, the Otsu method is applied on the image multi-channel to remove the background of the tongue accurately as the input of the model. At the same time, data augmentation is applied to avoid over-fitting of the model and improve the accuracy of model classification. The accuracy of the trained tongue color classifier based on CNN is 90.5% in the clinically collected data set, which is better than the traditional machine learning methods for tongue color classification.
Supported by key project at central government level (2060302).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Zhou, L., Zhang, P., Cheng, B.: Automatic tongue color analysis of traditional Chinese medicine based on image retrieval. In: 13th International Conference on Control Automation Robotics Vision (ICARCV), pp. 637–641. IEEE, Singapore (2015)
Tao, X.: Tongue image classification based on rough set theory. Comput. Eng. Appl. 43(27), 216–19 (2007)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9. IEEE, Boston (2015)
Jiang, S., Hu, J., Xia, C., Qi, J., Peng, Y.: A tongue image separation method based on Otsu threshold method and morphological adaptive correct. Comput. Intell. Neurosci. 10(1155), 102–106 (2016)
Harangi, B.: Skin lesion classification with ensembles of deep convolutional neural networks. J. Biomed. Inform. 86, 25–32 (2018)
ISO 36642000 Graphic technology and photography–Viewing conditions
Sladojevic, S., Arsenovic, M., Anderla, A.: Deep neural networks based recognition of plant diseases by leaf image classification. Chin. High Technol. Lett. 27(2), 150–155 (2017)
Zhang, L., Qin, J., Zeng, Y.: Tongue-coating image segmentation based on combination of morphological gradient and watershed algorithms. Imaging Sci. J. 59(6), 311–316 (2011)
Wu, K., Zhang, D.: Robust tongue segmentation by fusing region-based and edge-based approaches. Expert Syst. Appl. 42(21), 8027–8038 (2015)
Pham, T.-C., Luong, C.-M., Visani, M., Hoang, V.-D.: Deep CNN and data augmentation for skin lesion classification. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 573–582. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_54
Zhang, X., Shen, L.: Application of weighted SVM on the classification and recognition of tongue images. Chin. J. Biomed. Eng. 2006(02), 230–233 (2006)
Liang, J., Yang, H., Zhang, H.: The classification of common tongue body and tongue coating based on the feature of color. Microcomput. Appl. 36(17), 102–105 (2017)
Hui, K., Li, W., Shi, G.: Color classification of tongue based on PCA-AdaBoost in traditional Chinese medicine. J. Guangxi Normal Univ. 27(3), 158–161 (2009)
Smirnov, E.A., Timoshenko, D.M., Andrianov, S.N.: Comparison of regularization methods for imagenet classification with deep convolutional neural networks. In: 2nd AASRI Conference on Computational Intelligence and Bioinformatics (CIB), pp. 89–94. Elsevier Science, South Korea (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shang, Y., Mao, X., Zhao, Y., Li, N., Wang, Y. (2020). Classification of Tongue Color Based on Convolution Neural Network. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-3415-7_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3414-0
Online ISBN: 978-981-15-3415-7
eBook Packages: Computer ScienceComputer Science (R0)