EHO-Q-LGAN: An EHO-Based Q-Learning GAN for the Timely Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR) stands as a significant factor in the prevalence of blindness on a global scale, therefore, the timely identification and precise diagnosis of this condition are imperative for enabling proper management and care. Over the past few years, diverse methods have been employed for automated segmentation and classification of internal retinal components, but achieving high accuracy and efficiency remains a challenging task for clinical scenarios. This research introduces an innovative method for identifying and classifying internal retinal components that combine three advanced models: a dynamic segmentation model, an efficient Elephant Herding Optimization (EHO) Model, and an autoencoder model with Q-Learning Generative Adversarial Network (Q-LGAN) process. The dynamic segmentation model accurately identifies internal retinal components by incorporating advanced methods for image processing such as vessel enhancement, multi-scale analysis, and adaptive thresholding processes. The EHO Model extracts and selects multidimensional features from the segmented images, and the autoencoder model with Q-LGAN optimizes the classification and severity identification process. The proposed approach was evaluated on the EyePACS dataset and achieved efficient results in the context of accuracy, sensitivity, and specificity levels. Compared to existing methods, our approach demonstrated significant improvements in segmentation Accuracy and feature extraction efficiency, as well as improved classification and DR severity identification levels.