Retinal Image Enhancement Through Hyperparameter Selection Using RSO for CLAHE to Classify Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the main causes of blindness in diabetes patients.To choose the appropriate course of action for the patient, the severity level of the problem must be verified to avoid vision loss.Deep learning models are being utilized to classify the stage of DR at which the patient is right now.However, the input image to be fed to the Convolutional Neural Network (CNN) must be of good quality to facilitate subsequent tasks such as image segmentation, feature extraction, and classification.Most image enhancement techniques rely on Histogram modification, especially contrast-limited adaptive histogram equalization (CLAHE), to perform local contrast enhancement in order to avoid the drawbacks of existing methods.With Improper selection of hyperparameters such as the number of tiles and clipping limit, an image's quality decreases and becomes inappropriate for further processing.The uniqueness of Random Search Optimization (RSO) lies in its ability to dynamically adapt the search process based on the behavior of virtual rat swarms, allowing for effective exploration of the parameter space while balancing exploration and exploitation.RSO's advantage over existing methods lies in its ability to effectively handle high-dimensional and nonlinear search spaces, potentially leading to the discovery of optimal hyperparameter configurations that enhance retinal images and improve diabetic retinopathy classification accuracy.This study proposes an optimized clipping limit selection using the Rat Swarm Optimization algorithm for contrast-limited adaptive histogram equalization to enhance the retinal fundus images.The enhanced images are further segmented using Otsu thresholding for classification using CNN.The proposed model was evaluated and tested on the fundus datasets MESSIDOR and IDRiD.The result shows that the proposed optimized clipping limit selection using the Rat Swarm Optimization algorithm model outperforms with 7.33 MSE, 44.51 PNSR, and 0.89 SSIM values when compared to other methods.Classification accuracy was also improved by enhancing the fundus images.