An Efficient Feature Selection and Extraction using Metaheuristic Technique for Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR) is associated with diabetes, which causes harm to the retina as a result of persistent elevated blood sugar levels, resulting in symptoms such as impaired vision. Regular eye examinations are crucial for the timely identification of any issues. The process of selecting characteristics in DR involves optimizing important features using a combination of Hybridized Weight-Optimized Particle Swarm Optimization and Whale Optimization Algorithm (HWOPSO-WOA). This leads to an enhancement in the accuracy of detection, which is crucial for appropriate diagnosis and therapy. Pre-processing encompasses the utilization of linear filters such as Sobel and Prewitt for image manipulation, mean filters for the reduction of noise, and Gaussian filters for the purpose of smoothing. Feature selection employs an objective function that considers significant performance measure, which utilizes binary encoding and conducts fitness evaluation. The methodology is utilized for the analysis of DR utilizing the Indian Diabetic Retinopathy Image Dataset. The results comprise convergence graphs, and a comparative analysis, which emphasize the greater accuracy and efficiency of the Proposed technique. Visual representations, such as fundus images with selected features, highlight the importance of the chosen features in detecting DR. The proposed HWOPSO-WOA achieves highest accuracy of 97.3% and minimal processing time 98.45seconds that outperforms the state-of-art techniques.