Research on Multiple-Instance Learning for Tongue Coating Classification
Yonghui Tang, Yue Sun, John Y. Chiang, Xiaoqiang Li
Abstract
Tongue coating can provide valuable diagnostic information to reveal the disorder of the internal body. However, tongue coating classification has long been a challenging task in Traditional Chinese Medicine (TCM) due to the fact that tongue coatings are polymorphous, different tongue coatings have different colors, shapes, textures and locations. Most existing analyses utilize handcrafted features extracted from a fixed location, which may lead to inconsistent performance when the size or location of the tongue coating region varies. To solve this problem, this paper proposes a novel paradigm by employing artificial intelligence to feature extraction and classification of tongue coating. It begins with exploiting prior knowledge of rotten-greasy tongue coating to obtain suspected tongue coating patches. Based on the resulting patches, tongue coating features extracted by Convolutional Neural Network (CNN) are used instead of handcrafted features. Moreover, a multiple-instance Support Vector Machine (MI-SVM) which can circumvent the uncertain location problem is applied to tongue coating classification. Experimental results demonstrate that the proposed method outperforms state-of-the-art tongue coating classification methods.