Intelligent Diabetes Detection System based on Tongue Datasets

Author(s): Safia Naveed* , Gurunathan Geetha .

Journal Name: Current Medical Imaging

Volume 15 , Issue 7 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Background: Scanning Electron Microscope (SEM) Camera Imaging shows and helps analyze hidden organs in the human body. SEM image analysis provides in-depth and critical details of organ abnormalities. Similarly, the human tongue finds use in the detection of organ dysfunction with tongue reflexology.

Objective: To detect diabetes at an early stage using a non-invasive method of diabetes detection through tongue images and to utilize the reasonable cost of modality (SEM camera) for capturing the tongue images instead of the existing and expensive imaging modalities like X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, Single-Photon Emission Computed Tomography etc.

Methods: The tongue image is captured via SEM camera, it is preprocessed to remove noise and resize the tongue such that it is suitable for segmentation. Greedy Snake Algorithm (GSA) is used to segment the tongue image. The texture features of the tongue are analyzed and finally it is classified as diabetic or normal.

Results: Failure of organs stomach, intestine, liver and pancreas results in change of the color of the tongue, coating thickness and cracks on the tongue. Changes in pancreas proactive behavior also reflect on tongue coating. The tongue coating texture varies from white or vanilla to yellow also the tongue coating thickness also increases.

Conclusion: In this paper, the author proposes to diagnose Diabetes Type2 (DT2) at an early stage from tongue digital image. The tongue image is acquired and processed with Greedy Snake Algorithm (GSA) to extract edge and texture features.

Keywords: Scanning Electron Microscope Camera (SEM) Imaging, Diabetes-Type2 (DT2), Tongue Diabetes, Greedy Snake Algorithm (GSA), glucose, blood sugar.

Anastasia K, Soffia G, Araz R, et al. Type 1 diabetes mellitus. Nat Rev Dis Primers 2017: 17016(2017)
Dendup T, Feng X, Clingan S, Astell-Burt T. Environmental risk factors for developing type 2 diabetes mellitus: A systematic review. Int J Environ Res Public Health 2018; 15(1): 1-25.
[] [PMID: 29304014]
Koning SH, Hoogenberg K, Lutgers HL, van den Berg PP, Wolffenbuttel BH. Gestational diabetes mellitus: current knowledge and unmet needs. J Diabetes 2016; 8(6): 770-81.
[] [PMID: 27121958]
Wu Y, Ding Y, Tanaka Y, Zhang W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci 2014; 11(11): 1185-200.
[] [PMID: 25249787]
Janghorbani M, Amini M. Normal fasting plasma glucose and risk of prediabetes and type 2 diabetes: the Isfahan diabetes prevention study. Rev Diabet Stud 2011; 8(4): 490-8.
[] [PMID: 22580730]
Baird JD, Duncan LJ. The glucose tolerance test. Postgrad Med J 1959; 35(403): 308-14.
[] [PMID: 13657810]
Chiu C. The development of a computerized tongue diagnosis system. Biomed Eng Appl Basis Commun 1996; (8): 342-50.
Pei-Yung L, Po-Chi H. Jia-M, Chen J, Y.Chiang L,Chien L. Diabetes with pyogenic liver abscess - A perspective on tongue assessment in traditional Chinese medicine. Complement Ther Med 2014; 22(2): 341-8.
Praful PP, Pradyut KS, Sudeep KS, Arijit D, Sourangshu B, Swapna B. Cloud computing-based non-invasive glucose monitoring for diabetic care. IEEE Trans Circuits Syst I Regul Pap 2018; 65(2): 1-14.
Demitri N, Zoubir AM. Measuring blood glucose concentrations in photometric glucometers requiring very small sample volumes. IEEE Trans Biomed Eng 2017; 64(1): 28-39.
[] [PMID: 26955010]
Moreno EM, Lujan MJ, Rusinol MT, et al. Type 2 diabetes screening test by means of a pulse oximeter. IEEE Trans Biomed Eng 2017; 64(2): 341-51.
[] [PMID: 28113188]
Sunghoon J, Kenneth M, Hong L. A new approach to present a non-invasive optical glucose sensor using advanced opto-electronic technology. IJERI 2010; 2(1): 1-7.
Bottoni U, Tiriolo R, Pullano SA, et al. Infrared saliva analysis of psoriatic and diabetic patients: Similarities in protein components. IEEE Trans Biomed Eng 2016; 63(2): 379-84.
[] [PMID: 26208262]
Wenjun Z, Yunqing D, Ming LW. Noninvasive glucose monitoring using saliva nano-biosensor. Sens Biosensing Res 2015; 4: 23-9.
Agurto C, Murray V, Barriga E, et al. Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans Med Imaging 2010; 29(2): 502-12.
[] [PMID: 20129850]
Jiawei X, Xiaoqin Z, Huiling C, et al. Automatic analysis of microaneurysms turnover to diagnose the progression of diabetic retinopathy. IEEE Access 2018; 6: 9632-42.
Lucisano JY, Routh TL, Lin JT, Gough DA. Glucose Monitoring in Individuals With Diabetes Using a Long-Term Implanted Sensor/Telemetry System and Model. IEEE Trans Biomed Eng 2017; 64(9): 1982-93.
[] [PMID: 27775510]
Abd-Elraheem SE, El Saeed AM, Mansour HH. Salivary changes in type 2 diabetic patients. Diabetes Metab Syndr 2017; 11(2): S637-41.
[] [PMID: 28511885]
Liu X, Zhang H, Ren L, et al. Functional assessment of the stenotic carotid artery by CFD-based pressure gradient evaluation. Am J Physiol Heart Circ Physiol 2016; 311(3): H645-53.
[] [PMID: 27371686]
Wong KCL, Wang L, Zhang H, Liu H, Shi P. Physiological fusion of functional and structural images for cardiac deformation recovery. IEEE Trans Med Imaging 2011; 30(4): 990-1000.
[] [PMID: 21224172]
Liu X, Gao Z, Xiong H, et al. Three-dimensional hemodynamics analysis of the circle of Willis in the patient-specific nonintegral arterial structures. Biomech Model Mechanobiol 2016; 15(6): 1439-56.
[] [PMID: 26935302]
Gao Z, Hau WK, Lu M, et al. Automated framework for detecting lumen and media–adventitia borders in intravascular ultrasound images. Ultrasound Med Biol 2015; 41(7): 2001-21.
Kemal P, Salih G. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digit Signal Process 2007; 17(4): 702-10.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [672 - 678]
Pages: 7
DOI: 10.2174/1573405614666181009133414
Price: $58

Article Metrics

PDF: 17