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Age-related macular degeneration diagnosis in optical coherence tomography images with gray level co-occurrence matrix features, genetic algorithms, and random forest classifier.

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Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods.

Loganathan R1, Latha S1

  • 1Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu District, Tamil Nadu, India.

Biomedical Physics & Engineering Express
|January 8, 2025
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Summary

This study enhances age-related macular degeneration (AMD) detection using texture analysis from retinal images. Machine learning with Grey Level Co-occurrence Matrix (GLCM) features significantly improves diagnostic accuracy.

Keywords:
GLCMage-related macular degenerationmachine learning algorithmsophthalmologyrandom forestretinal images

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Science

Background:

  • Age-related macular degeneration (AMD) is a leading cause of global blindness, severely impacting visual acuity and life expectancy.
  • Increasing AMD prevalence necessitates advancements in diagnostic and prognostic tools to improve patient outcomes.
  • Current diagnostic methods require enhancement for greater accuracy and efficiency.

Purpose of the Study:

  • To improve the diagnosis of age-related macular degeneration (AMD) in preprocessed retinal images.
  • To evaluate the effectiveness of Grey Level Co-occurrence Matrix (GLCM) features for texture analysis in AMD detection.
  • To compare the performance of different feature sets and machine learning models for AMD classification.

Main Methods:

  • Utilized Optical Coherence Tomography (OCT) image datasets for analysis.
  • Employed Grey Level Co-occurrence Matrix (GLCM) features, including contrast, dissimilarity, correlation, energy, and homogeneity, for texture analysis.
  • Applied supervised machine learning (ML) algorithms and Convolutional Neural Network (CNN) techniques, comparing GLCM features, selected GLCM features, and grayscale pixel features (GSF).

Main Results:

  • Models using GSF features demonstrated low accuracy (e.g., 23-54%).
  • GLCM features significantly improved accuracy, achieving up to 98% with Random Forest (RF) and 83% with CNN.
  • Selected Fine Grey Level Co-occurrence Matrix (SFGLCM) features yielded the best performance, reaching 98% accuracy for OCTID detection across both RF and CNN models.

Conclusions:

  • SFGLCM and GLCM features substantially outperform GSF in AMD detection, enhancing accuracy and generalization.
  • The study highlights the potential of machine learning, particularly texture analysis via GLCM, to significantly improve AMD diagnosis.
  • This Python-based research demonstrates the value of ML in ophthalmology for better patient outcomes.