Hybrid model of feature-driven modular neural network-based grasshopper optimization algorithm for diabetic retinopathy classification using fundus images.

Abstract

Diabetic retinopathy (DR) is a progressive condition that can lead to blindness if undiagnosed or untreated. Automatic systems for DR prediction using fundus images have been developed, but challenges like variable illumination, overfitting, small datasets, poor feature learning, high computational complexity, and suboptimal feature weighting persist. To address these, a hybrid model called the modular neural network with grasshopper optimization algorithm (MNN-GOA) is proposed. This model integrates neural network capabilities with the grasshopper optimization algorithm (GOA) to enhance feature selection and classification accuracy. It begins with preprocessing to improve image quality, followed by data augmentation and histogram-based segmentation to focus on critical regions. Features are extracted using techniques like histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), color features, and mutual information (MI). GOA optimizes feature weights, balancing exploration and exploitation, while reducing computational complexity. The model integrates features from ground truth and original images to predict DR stages accurately. Achieving performance metrics of accuracy (98.8%), specificity (97.6%), sensitivity (96.8%), precision (96.4%), and F1 score (96.2%), the MNN-GOA model was validated on four datasets like DIARETDB1, DDR, APTOS 2019, and EyePACS and outperformed existing methods, proving to be a robust and efficient solution for DR classification and severity prediction.