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Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region.

L Aneesh Euprazia1, A Rajeswari1, K K Thyagharajan2

  • 1Computer Science and Engineering, Velammal Engineering College, Chennai, India.

Journal of Diabetes Research
|October 5, 2023
PubMed
Summary
This summary is machine-generated.

Vision diabetology analyzes facial skin to detect diabetes mellitus (DM). A novel multiwavelet transform convolutional neural network (MWTCNN) achieved 98.3% accuracy, offering a non-invasive method for continuous glucose monitoring.

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Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Endocrinology

Background:

  • Diabetes mellitus (DM) is characterized by high blood glucose levels, leading to complications affecting facial skin due to altered blood flow.
  • Facial skin changes associated with DM, including Bell's palsy, rubeosis faciei, scleroderma, and vitiligo, present diagnostic challenges.
  • Current diagnostic methods for DM often involve invasive procedures like serum glucose testing.

Purpose of the Study:

  • To develop and validate a non-invasive method for analyzing diabetes mellitus (DM) using facial skin imaging.
  • To propose a novel deep learning model for accurate diabetic measurement from facial images.
  • To enable continuous glucose monitoring through vision diabetology.

Main Methods:

  • Collected 200 facial images from diabetic patients in India, including those with specific skin conditions.
  • Utilized the Viola Jones algorithm for face detection and HSV color model segmentation to identify affected skin areas.
  • Developed and applied a multiwavelet transform convolutional neural network (MWTCNN) for feature extraction and diabetic measurement.

Main Results:

  • The proposed MWTCNN model demonstrated superior performance compared to existing deep learning models.
  • Achieved a high diagnostic accuracy of 98.3% for diabetes mellitus detection using facial images.
  • Successfully extracted relevant features from scaled facial images for diabetic measurement.

Conclusions:

  • Facial skin region analysis offers a promising non-invasive approach for diabetes mellitus detection and monitoring.
  • The MWTCNN model provides a highly accurate and efficient tool for vision diabetology.
  • This method eliminates the need for serum pricking, facilitating continuous glucose monitoring.