Classification of Body Constitution Based on TCM Philosophy and Deep Learning


There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy.

Conclusions and Future Work

In this paper, we proposed a non-invasive holistic iris-based human health assessment system based on the philosophy of TCM. This study combined the technique of iris image processing, TCM, and iridology to classify the human body constitution using a deep-learning-based algorithm. Furthermore, a real-time operable system based on the proposed method in this study was implemented to show the effectiveness and practicability of this study. The main advantage of the proposed system is that it helps practitioners to make a quick analysis and prompt classification of the subject’s body constitution and also gives instant feedback about how to maintain health. The system is a user-friendly and portable tool that can predict potential internal organ disorders and provide personal health care suggestions. The experimental results demonstrated that our iris-based method is effective and efficient on the CASIA-Iris-Thousand database. The highest achieved accuracy was 82.9% on the DenseNet201 CNN model.
In the future, we will attempt to develop new CNN architectures that will be able to learn the features of the nine classes more efficiently so that we can train our model with a lesser amount of data. In addition, we plan to publish our annotated database of the nine classes so that the research community of iridology can benefit from the labeled data.


Authors: Yung-Hui Li [OrcID] , Muhammad Saqlain Aslam *, Kai-Lin Yang, Chung-An Kao and Shin-You Teng
Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan

Symmetry 2020, 12(5), 803;
Received: 8 April 2020 / Revised: 29 April 2020 / Accepted: 6 May 2020 / Published: 12 May 2020

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