Complexity perception classification method for tongue constitution recognition

The body constitution is much related to the diseases and the corresponding treatment programs in Traditional Chinese Medicine. It can be recognized by the tongue image diagnosis, so that it is essentially regarded as a problem of tongue image classification, where each tongue image is classified into one of nine constitution types. This paper first presents a system framework to automatically identify the constitution through natural tongue images, where deep convolutional neural networks are carefully designed for tongue coating detection, tongue coating calibration, and constitution recognition. Under the system framework, a novel complexity perception (CP) classification method is proposed to nicely perform the constitution recognition, which can better deal with the bad influence of the variation of environmental condition and the uneven distribution of the tongue images on constitution recognition performance. CP performs the constitution recognition based on the complexity of individual tongue images by selecting the classifier with the corresponding complexity. To evaluate the performance of the proposed method, experiments are conducted on three sizes of clinic tongue images from hospitals. The experimental results illustrate that CP is effective to improve the accuracy of body constitution recognition.

[1]  Chia Yee Ooi,et al.  Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis , 2016, International Workshop on Pattern Recognition.

[2]  Yingshuai Li,et al.  Cognition research and constitutional classification in Chinese medicine. , 2011, The American journal of Chinese medicine.

[3]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Xin Liu,et al.  The study of the constitution, mucosal inflammation, Chinese medicine syndrome types and clinical pathology in IgA nephropathy , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  David Zhang,et al.  A high quality color imaging system for computerized tongue image analysis , 2013, Expert Syst. Appl..

[6]  Lingru Li,et al.  Classification of traditional Chinese medicine constitution based on facial features in color images , 2016 .

[7]  Ling Zhu,et al.  Knowledge graph for TCM health preservation: Design, construction, and applications , 2017, Artif. Intell. Medicine.

[8]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Ye Duan,et al.  Computer-aided tongue image diagnosis and analysis , 2012 .

[10]  Kun Zhang,et al.  Lung sounds classification using convolutional neural networks , 2018, Artif. Intell. Medicine.

[11]  Kun-Chan Lan,et al.  Color Correction Parameter Estimation on the Smartphone and Its Application to Automatic Tongue Diagnosis , 2015, Journal of Medical Systems.

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Guo-Zheng Li,et al.  Computerized tongue image segmentation via the double geo-vector flow , 2014, Chinese Medicine.

[14]  Bo Peng,et al.  An SA-GA-BP neural network-based color correction algorithm for TCM tongue images , 2014, Neurocomputing.

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Junwen Zhang,et al.  Extraction of tongue feature related to TCM physique based on image processing , 2015, 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP).

[17]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Li Zhuo,et al.  A K-PLSR-based color correction method for TCM tongue images under different illumination conditions , 2016, Neurocomputing.

[19]  Fan Lin,et al.  TCM clinic records data mining approaches based on weighted-LDA and multi-relationship LDA model , 2016, Multimedia Tools and Applications.

[20]  Dong Xu,et al.  An automatic tongue detection and segmentation framework for computer-aided tongue image analysis , 2011, 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services.

[21]  John Y. Chiang,et al.  The Study on the Agreement between Automatic Tongue Diagnosis System and Traditional Chinese Medicine Practitioners , 2012, Evidence-based complementary and alternative medicine : eCAM.

[22]  Ying Dai.  Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains , 2017 .

[23]  A Study of Tongue and Pulse Diagnosis in Traditional Korean Medicine for Stroke Patients Based on Quantification Theory Type II , 2013, Evidence-based complementary and alternative medicine : eCAM.

[24]  David Zhang,et al.  Detecting Diabetes Mellitus and Nonproliferative Diabetic Retinopathy Using Tongue Color, Texture, and Geometry Features , 2014, IEEE Transactions on Biomedical Engineering.

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Lingru Li,et al.  Phlegm-dampness constitution: genomics, susceptibility, adjustment and treatment with traditional Chinese medicine. , 2013, The American journal of Chinese medicine.

[27]  Ying Dai.  Measuring the Quality of Semi-subjective Training Data and Its Application in the Inference of TCM Zhengs , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[28]  Er-Yang Huan,et al.  Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification , 2019, Comput. Math. Methods Medicine.

[29]  Ying Dai.  Quality assessment of subjectively labelled training data for improving the reliability of status prediction , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[30]  Ziba Gandomkar,et al.  MuDeRN: Multi-category classification of breast histopathological image using deep residual networks , 2018, Artif. Intell. Medicine.

[31]  David Zhang,et al.  Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics , 2013, IEEE Transactions on Image Processing.

[32]  Jiajun Zhu,et al.  A new tongue diagnosis application on Android platform , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[33]  David Zhang,et al.  An Optimized Tongue Image Color Correction Scheme , 2010, IEEE Transactions on Information Technology in Biomedicine.

[34]  David Zhang,et al.  Tongue shape classification by geometric features , 2010, Inf. Sci..

[35]  Tao Chen,et al.  Latent tree models and diagnosis in traditional Chinese medicine , 2008, Artif. Intell. Medicine.

[36]  Tony R. Martinez,et al.  An instance level analysis of data complexity , 2014, Machine Learning.