Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, necessitating regular screenings to prevent its progression to severe stages. Manual diagnosis is labor-intensive and prone to inaccuracies, highlighting the need for automated, accurate detection methods. This study proposes a novel approach for early DR detection by integrating advanced machine learning techniques. The proposed system employs a three-phase methodology: initial image preprocessing, blood vessel segmentation using a Hopfield Neural Network (HNN), and feature extraction through an Attention Mechanism-based Capsule Network (AM-CapsuleNet). The features are optimized using a Taylor-based African Vulture Optimization Algorithm (AVOA) and classified using a Bilinear Convolutional Attention Network (BCAN). To enhance classification accuracy, the system introduces a hybrid Electric Fish Optimization Arithmetic Algorithm (EFAOA), which refines the exploration phase, ensuring rapid convergence. The model was evaluated on a balanced dataset from the APTOS 2019 Blindness Detection challenge, demonstrating superior performance in terms of accuracy and efficiency. The proposed system offers a robust solution for the early detection and classification of DR, potentially improving patient outcomes through timely and precise diagnosis.