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Comparative analysis of classification based algorithms for diabetes diagnosis using iris images.

Piyush Samant1, Ravinder Agarwal1

  • 1a Electrical and Instrumentation Engineering Department , Thapar University , Patiala , Punjab , India.

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|January 5, 2018
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Summary

This study explores iris image analysis for diabetic health assessment. Advanced image processing techniques can detect iris pigmentation changes, aiding in non-invasive diabetic diagnosis with 89.66% accuracy.

Keywords:
Iridology statistical analysisIrisclassificationdisease diagnosiswavelet features

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Photo-diagnosis is an emerging field leveraging image processing and computer vision.
  • Iris recognition typically focuses on overall structure, but diagnostic applications require analysis of local variations.

Purpose of the Study:

  • To investigate changes in iris pigmentation features for diabetic health assessment.
  • To develop a non-invasive, automated method for diabetic diagnosis using iris imaging.

Main Methods:

  • Applied pre-image processing to extract iris regions of interest.
  • Extracted statistical, textural, and wavelet features from the iris regions.
  • Compared the classification accuracy of five different machine learning classifiers.

Main Results:

  • The random forest classifier achieved the highest classification accuracy of 89.66%.
  • The proposed methodology demonstrated effectiveness and diagnostic significance for diabetic detection.

Conclusions:

  • Iris feature analysis, particularly pigmentation irregularities, shows promise for non-invasive diabetic diagnosis.
  • The developed approach offers a novel systemic perspective for automated diabetic screening.