  Background and Object: In China, body constitution is highly related to
physiological and pathological functions of human body and determines the
tendency of the disease, which is of great importance for treatment in clinical
medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine
inspection, is an important way to recognize the type of constitution.In order
to deploy tongue image constitution recognition system on non-invasive mobile
device to achieve fast, efficient and accurate constitution recognition, an
efficient method is required to deal with the challenge of this kind of complex
environment. Methods: In this work, we perform the tongue area detection,
tongue area calibration and constitution classification using methods which are
based on deep convolutional neural network. Subject to the variation of
inconstant environmental condition, the distribution of the picture is uneven,
which has a bad effect on classification performance. To solve this problem, we
propose a method based on the complexity of individual instances to divide
dataset into two subsets and classify them separately, which is capable of
improving classification accuracy. To evaluate the performance of our proposed
method, we conduct experiments on three sizes of tongue datasets, in which deep
convolutional neural network method and traditional digital image analysis
method are respectively applied to extract features for tongue images. The
proposed method is combined with the base classifier Softmax, SVM, and
DecisionTree respectively. Results: As the experiments results shown, our
proposed method improves the classification accuracy by 1.135% on average and
achieves 59.99% constitution classification accuracy. Conclusions: Experimental
results on three datasets show that our proposed method can effectively improve
the classification accuracy of tongue constitution recognition.
