ArticleComputational and mathematical methods in medicine2015
ROC-Boosting: A Feature Selection Method for Health Identification Using Tongue Image.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Lightweight YOLOv8 for tongue teeth marks and fissures detection based on C2f_DCNv3.Scientific reports · 2025Article
- Microbial community structure of different tongue fur types in patients with chronic gastritis.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2023Article
- A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.Micromachines · 2022Article
- Can Traditional Chinese Medicine Diagnosis Be Parameterized and Standardized? A Narrative Review.Healthcare (Basel, Switzerland) · 2021Review
- Comparison of the Outcomes of Three Different Nutritional Supports in Patients with Oral and Maxillofacial Malignant Tumors following Surgery.Evidence-based complementary and alternative medicine : eCAM · 2018Article
- Potential screening and early diagnosis method for cancer: Tongue diagnosis.International journal of oncology · 2016Article
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3 authors.
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Abstract
objectiveTo select significant Haar-like features extracted from tongue images for health identification. MATERIALS AND
methods1,322 tongue cases were included in this study. Health information and tongue images of each case were collected. Cases were classified into the following groups: group containing 148 cases diagnosed as health; group containing 332 cases diagnosed as ill based on health information, even though tongue image is normal; and group containing 842 cases diagnosed as ill. Haar-like features were extracted from tongue images. Then, we proposed a new boosting method in the ROC space for selecting significant features from the features extracted from these images.
resultsA total of 27 features were obtained from groups A, B, and C. Seven features were selected from groups A and B, while 25 features were selected from groups A and C.
conclusionsThe selected features in this study were mainly obtained from the root, top, and side areas of the tongue. This is consistent with the tongue partitions employed in traditional Chinese medicine. These results provide scientific evidence to TCM tongue diagnosis for health identification.
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