Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes

Intelligent Diabetes Detection System based on Tongue Datasets

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.