Improved Grey Wolf Optimization-Based Feature Selection and Classification Using CNN for Diabetic Retinopathy Detection

This research offers a new prediction structure coupling improved grey wolf optimization (IGWO) and convolutional neural network (CNN), called IGWO-CNN, to diagnose diabetic retinopathy. Grey wolf optimizer (GWO) is achieving success among other swarm intelligence procedures due to its broad tuning features, scalability, simplicity, ease of use and, most importantly, its ability to ensure convergence speed by providing suitable exploration and exploitation throughout a search. The suggested methodology used a genetic algorithm (GA) to build diversified initial positions. GWO was subsequently applied to adjust existing population positions in the discrete search procedure, getting the optimal feature subset for a higher CNN-based classification challenge. The presented technique contrasts with GA, GWO and numerous existing state-of-the-art diabetic retinopathy classification approaches. The suggested strategy outperforms all other methods by increasing classification accuracy to 98.33%, indicating its efficacy in detecting the DR. The simulation outcomes have shown that the proposed approach outperforms the other two competing methods.