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Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection
Li, Jinxing; Zhang, David; Li, Yongcheng; Wu, Jian; Zhang, Bob
2017-04
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
Volume384Pages:191-204
AbstractEffective and accurate diagnosis of Diabetes Mellitus (DM), as well as its early stage Impaired Glucose Regulation (IGR), has attracted much attention recently. Traditional Chinese Medicine (TCM) [Bob Zhang, BVK Kumar, and David Zhang. Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features. Biomedical Engineering, IEEE Transactions on, 61(2):491-501, 2014.], [Bob Zhang, BVK Kumar, and David Zhang. Noninvasive diabetes mellitus detection using facial block color with a sparse representation classifier. Biomedical Engineering, IEEE Transactions on, 61(4):1027-1033, 2014.] etc. has proved that tongue, face and sublingual diagnosis as a noninvasive method is a reasonable way for disease detection. However, most previous works only focus on a single task (tongue, face or sublingual) for diagnosis, although different tasks may provide complementary information for the diagnosis of DM and IGR. In this paper, we propose a novel fusion method to jointly represent the tongue, face and sublingual information and discriminate between DM (or IGR) and healthy controls. Specially, the tongue, facial and sublingual images are first collected by using a non-invasive capture device. The color, texture and geometry features of these three types of images are then extracted, respectively. Finally, our so-called joint similar and specific learning (JSSL) approach is proposed to combine features of tongue, face and sublingual vein, which not only exploits the correlation but also extracts individual components among them. Experimental results on a dataset consisting of 192 Healthy, 198 DM and 114 IGR samples (all samples were obtained from Guangdong Provincial Hospital of Traditional Chinese Medicine) substantiate the effectiveness and superiority of our proposed method for the diagnosis of DM and IGR, achieving 86.07% and 76.68% in average accuracy and 0.8842 and 0.8278 in area tinder the ROC curves, respectively. The source code can be found in https://github.com/sasky1/JSSLreleased. (C) 2016 Published by Elsevier Inc.
KeywordDiabetes mellitus (DM) Impaired glucose regulation (IGR) joint representation Tongue image Facial image Sublingual image
DOI10.1016/j.ins.2016.09.031
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000392785100012
PublisherELSEVIER SCIENCE INC
The Source to ArticleWOS
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Citation statistics
Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Li, Jinxing,Zhang, David,Li, Yongcheng,et al. Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection[J]. INFORMATION SCIENCES,2017,384:191-204.
APA Li, Jinxing,Zhang, David,Li, Yongcheng,Wu, Jian,&Zhang, Bob.(2017).Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection.INFORMATION SCIENCES,384,191-204.
MLA Li, Jinxing,et al."Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection".INFORMATION SCIENCES 384(2017):191-204.
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