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Related Experiment Video

Updated: Sep 1, 2025

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Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Musatafa Abbas Abbood Albadr1, Masri Ayob1, Sabrina Tiun1

  • 1Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.

Frontiers in Public Health
|August 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for detecting Diabetic Retinopathy (DR) using the Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) and Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA). The combined method significantly improves DR detection accuracy in medical images.

Keywords:
Diabetic RetinopathyHistogram of Oriented GradientsPrincipal Component Analysisextreme learning machinegray wolf optimization

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Machine Learning

Background:

  • Diabetic Retinopathy (DR) detection accuracy requires improvement.
  • Machine Learning (ML) methods are widely used but need enhancement.
  • Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is accurate but not applied to DR detection.

Purpose of the Study:

  • To enhance Diabetic Retinopathy (DR) detection accuracy.
  • To apply the Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) classifier for DR detection.
  • To utilize Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA) for feature extraction in DR detection.

Main Methods:

  • Implemented the Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) classifier.
  • Employed Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA) for feature extraction.
  • Evaluated the model on APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD) for binary and multi-class classification.

Main Results:

  • Achieved 96.21% multi-class and 99.47% binary accuracy on APTOS-2019.
  • Attained 96.15% multi-class and 99.04% binary accuracy on IDRiD.
  • Demonstrated excellent performance for DR classification.

Conclusions:

  • The combination of GWO-ELM and HOG-PCA is effective for DR detection.
  • This approach offers high accuracy in both binary and multi-class DR classification.
  • The proposed method shows potential for application to other image data types.