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

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

Abstract 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 stud...

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Bibliographic Details
Main Authors: Udayaraju Pamula, Venkateswararao Pulipati, G. Vijaya Suresh, M. V. Jagannatha Reddy, Anil Kumar Bondala, Srihari Varma Mantena, Ramesh Vatambeti
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
Subjects:
African vulture optimization
Attention mechanism based capsule network
Diabetic retinopathy
Hopfield neural network
Electric fish optimization
Bilinear convolutional attention network
Online Access:https://doi.org/10.1038/s41598-025-99228-w
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Summary:Abstract 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.
ISSN:2045-2322

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