AO-HRCNN: archimedes optimization and hybrid region-based convolutional neural network for detection and classification of diabetic retinopathy

Abstract

Diabetic Retinopathy (DR) primarily affects a set of lesions in the eyes, causing retinal degeneration and loss of vision. The DR features serve as a crucial component for ophthalmologists to diagnose DR at an earlier stage. This paper presents an automatic DR screening tool using a hybrid GAN-RCNN architecture formulated to categorize and identify different DR grades from the fundus images captured at various resolutions. The hybrid GAN-RCNN architecture is formulated by replacing the discriminator in the GAN with the RCNN classifier. The RCNN model can handle the complex inter-class and intra-class variations present in the fundus retina images and classify them into different classes such as mild, moderate, severe, and nonproliferative DR. The RCNN model not only extracts the pixels present in the fundus image but also focuses on the significant relationship that exists between different DR classes. The Archimedes optimization Algorithm (AOA) is used to optimize the different GAN and RCNN hyperparameters. When compared to the existing techniques the proposed model offers an accuracy of 99%, 98.5%, and 99.4% in the APTOS, Kaggle, and Messidor datasets which is comparatively high. The experimental outcome reveals that the introduced model serves as a concrete baseline for the diagnosis and treatment of DR patients.