A Hybrid GAN-BiGRU Model Enhanced by African Buffalo Optimization for Diabetic Retinopathy Detection

Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes mellitus, leading to vision impairment or even blindness if not diagnosed and treated early. A manual inspection of the patient's retina is the conventional way for diagnosing diabetic retinopathy. This study offers a novel method for the identification of diabetic retinopathy in medical diagnosis. Using a hybrid Generative Adversarial Network (GAN) and Bidirectional Gated Recurrent Unit (BiGRU) model, further refined using the African Buffalo Optimization algorithm, the model's capacity to identify minute patterns suggestive of diabetic retinopathy is improved by the GAN's skill in extracting complex characteristics from retinal pictures. The technique of feature extraction plays a critical role in revealing information that may be hidden yet is essential for a precise diagnosis. Then, the BiGRU part works on the characteristics that have been extracted, efficiently maintaining temporal relationships, and enabling thorough information absorption. The combination of GAN's feature extraction capabilities with BiGRU's sequential information processing capability creates a synergistic interaction that gives the model a comprehensive grasp of retinal pictures. Moreover, the African Buffalo Optimization technique is utilized to optimize the model's performance for improved accuracy in the identification of diabetic retinopathy by fine-tuning its parameters. The current study, which uses Python, obtains a 98.5% accuracy rate and demonstrates its amazing ability to reach high levels of accuracy in Diabetic Retinopathy Detection.