Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65400
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLi, J-
dc.creatorZhang, D-
dc.creatorLi, Y-
dc.creatorWu, J-
dc.creatorZhang, B-
dc.date.accessioned2017-05-22T02:08:32Z-
dc.date.available2017-05-22T02:08:32Z-
dc.identifier.issn0020-0255-
dc.identifier.urihttp://hdl.handle.net/10397/65400-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDiabetes mellitus (DM)en_US
dc.subjectFacial imageen_US
dc.subjectImpaired glucose regulation (IGR)en_US
dc.subjectJoint representationen_US
dc.subjectSublingual imageen_US
dc.subjectTongue imageen_US
dc.titleJoint similar and specific learning for diabetes mellitus and impaired glucose regulation detectionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage191-
dc.identifier.epage204-
dc.identifier.volume384-
dc.identifier.doi10.1016/j.ins.2016.09.031-
dcterms.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 under the ROC curves, respectively. The source code can be found in https://github.com/sasky1/JSSLreleased.-
dcterms.bibliographicCitationInformation sciences, 2017, v. 384, p. 191-204-
dcterms.isPartOfInformation sciences-
dcterms.issued2017-
dc.identifier.isiWOS:000392785100012-
dc.identifier.scopus2-s2.0-84994533164-
dc.identifier.ros2016002505-
dc.identifier.eissn1872-6291-
dc.identifier.rosgroupid2016002453-
dc.description.ros2016-2017 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201804_a bcma-
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