Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection

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.co