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Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection

V Desika Vinayaki¹, R Kalaiselvi¹

¹Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India.

Neural Processing Letters

|January 31, 2022

Summary

This summary is machine-generated.

A new framework accurately detects and classifies diabetic retinopathy (DR) stages using advanced algorithms. This method offers improved diagnostic performance for this common diabetes complication.

Keywords:

DRIVE database Diabetic retinopathy Faster R-CNN Multi threshold-based Remora Optimization Wild Geese Algorithm

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients.
Early detection and classification of DR are crucial for timely intervention and preventing blindness.

Purpose of the Study:

To propose a novel framework for the detection and classification of diabetic retinopathy (DR).
To evaluate the performance of the proposed framework against existing methods.

Main Methods:

The framework involves four stages: image pre-processing, vessel segmentation using the Multi threshold-based Remora Optimization (MTRO) algorithm, and feature extraction and classification using a Region-based Convolutional Neural Network (R-CNN) integrated with the Wild Geese Algorithm (WGA).
Experiments were conducted using images from the DRIVE database.

Main Results:

The proposed R-CNN with WGA achieved high performance in classifying DR stages (Non-DR, Mild DR, Moderate DR, Severe DR, Proliferative DR).
The framework demonstrated superior performance with an accuracy of 95.42%, specificity of 93.10%, sensitivity of 93.20%, and an F-score of 98.28%.
Performance surpassed existing methods including FCDNN, GSFS, CNN, and DL techniques.

Conclusions:

The developed R-CNN with WGA framework provides an effective and accurate solution for diabetic retinopathy detection and classification.
This novel approach shows significant potential for improving the diagnosis and management of diabetic retinopathy.