Research on Multiple-Instance Learning for Tongue Coating Classification
| Autor: | Xiaoqiang Li, Yonghui Tang, John Y. Chiang, Yue Sun |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Diagnostic information
General Computer Science Computer science Feature extraction 02 engineering and technology Convolutional neural network multiple-instance learning 030218 nuclear medicine & medical imaging 03 medical and health sciences deep features 0302 clinical medicine Tongue 0202 electrical engineering electronic engineering information engineering medicine General Materials Science business.industry Tongue coating classification General Engineering Pattern recognition TK1-9971 Support vector machine medicine.anatomical_structure Classification methods 020201 artificial intelligence & image processing Electrical engineering. Electronics. Nuclear engineering Tongue coating Artificial intelligence business |
| Zdroj: | IEEE Access, Vol 9, Pp 66361-66370 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3076604 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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