| dc.description.abstract | Tongue diagnosis is an important indicator of traditional Chinese medicine (TCM) syndrome differentiation and treatment. It is a simple and non-invasive examination. Sublingual vein (SV) is a symbol of TCM to judge sublingual blood stasis . There have been many theses showing that the degree of SV stasis is positively correlated with the severity of the disease. However, the diagnosis of SV is often due to subjective factors such as experience,color perception, and mentality among different physicians, resulting in different interpretation results. The goal of this research is to develop a computer-assisted system
based on machine learning to assist in diagnosing the degree of SV stasis in patients. We consider the problem of binary classification, mild and severe. We test many supervised
machine learning models, including support vector machines (SVM), K-nearest neighbor,decision trees, RidgeClassifier, etc. In order to improve accuracy and save training time,
this research uses two techniques to extract features. One is to use Principle Component Analysis (PCA) combined with Sliced Inverse Regression (SIR) for extraction. Another
way is using Convolutional Neural Network (CNN) for extraction. Experiments results have found that when using original photos and grayscale images, the average accuracy
is only about 60.5%. After removing the noise, and the red, green, and blue three channels are retained, the average accuracy of machine learning can reach 81%.Among the
thirteen models, the best model SVM_linear has an accuracy of 85.5%. In addition, we using PCA+SIR, with the accuracy maintained, the training time is only about 1/35 of
the original, achieving the goal of saving computing costs. Finally, We also use the CNN for feature extraction, and RidgeClassifier of the thirteen models has highest accuracy
87.5%. All of the above, with the combination of the confusion matrix and receiver operating characteristic curve (ROC curve), we can provide doctors a scientific system to
diagnose the severity of SV stasis . | en_US |