Glaucoma Detection with Improved Deep Learning Model Trained with Optimal Features: An Improved Meta-Heuristic Model

Authors

  • Raja Chandrasekaran Associate Professor, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamil Nadu, India
  • Santhosh Krishna B. V. Associate Professor, Department of CSE, New Horizon College of Engineering, Bengaluru, Karnataka, India
  • Balaji Loganathan Associate Professor, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, Tamil Nadu, India
  • Sanjay Kumar Suman Professor, Department of ECE, St. Martin’s Engineering College, Telangana, India
  • Bhagyalakshmi Professor, Department of ECE, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India

Keywords:

Glaucoma, Fundus Eye, OD and OC, Optimized K-Means clustering technique, Median Local Gradient Pattern-MLGP, DEEO, I-CNN

Abstract

Glaucoma is by far the most common retinal condition, wherein the intraocular pressure (IOP) on the eye damages the retina. Glaucoma damages ONH, which leads to visual impairment if not addressed. A skilled ophthalmologist checks the course of glaucoma on the retinal area of the eye. This method is time-consuming and inefficient. As a result, this is indeed a legitimate problem that can be addressed by using deep learning algorithms to automatically diagnose glaucoma. In this research work, a novel glaucoma detection model is developed by following five major phases: “(a) pre-processing, (b) ROI identification, (c) feature extraction, (d) feature selection, and (e) glaucoma classification (normal / diseased)”. Initially, the collected retinal images are pre-processed via wiener filtering (to remove noise) and CLAHE (for contrast enhancement). Then, ROI of pre-processed image is selected via Optimized K-Means clustering technique, wherein the centroids of K-means are optimally selected via Dingo with Enhanced Encircling Optimization Model (DEEO). Subsequently, the features inclusive of color feature (Color Histogram and Color Co-occurrence Matrix (CCM)), texture features (Local Binary Pattern-LBP, Median Local Gradient Pattern-MLGP) are extracted from the identified ROI areas. Further, among the selected features, the most relevant features are selected via Dingo with Enhanced Encircling Optimization Model (DEEO).  This DEEO is a conceptual expansion of standard Dingo Optimizer (DOX). Ultimately, using Improved CNN (I-CNN), the classification of OC and OD for healthy and diseased takes place precisely. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model.

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Stages of pre-processing

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Published

17.05.2023

How to Cite

Chandrasekaran, R. ., B. V., S. K. ., Loganathan, B. ., Suman, S. K. ., & Bhagyalakshmi. (2023). Glaucoma Detection with Improved Deep Learning Model Trained with Optimal Features: An Improved Meta-Heuristic Model. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 532–547. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2878

Issue

Vol. 11 No. 6s (2023)

Section

Research Article

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