A novel glaucoma detection model using Unet++-based segmentation and ResNet with GRU-based optimized deep learning

Abstract

Glaucoma is a dangerous eye disease and it affects the vision in people. It mainly damages the eye nerves and the severe cause of glaucoma leads to permanent sight loss. The target of the glaucoma disease detection approach is lowering the intraocular pressure and reducing inflammation. The detection of glaucoma disease takes more time. This method functioned based on the availability of energy and sample resources. However, the existing approaches have the chance for human error and misclassification results. Many computer vision-based techniques are used for glaucoma detection but, none of them are detected glaucoma disease in the early stage. Then, they focused on the Convolutional Neural Network (CNN) of segmentation and feature extraction for detecting glaucoma disease. In this research, a new glaucoma detection model with an ensemble learning architecture is implemented with the heuristic strategy. Then, the collected images are given into histogram equalization technique and Contrast Limited Adaptive Histogram Equalization (CLAHE) for pre-processing the retinal fundus images. The pre-processed images are separately considered for performing the disc and cup segmentation, where both the segmentation is done through U-Shape Network (Unet++) technique. The segmented disc and cup images preceded towards the glaucoma detection, in which the hybrid deep learning technique of Residual Network (ResNet) with Gated Recurrent Units (ResNet-GRU) is used for detecting the glaucoma disease. Here, certain parameters in the deep learning techniques for glaucoma detection are tuned with a Modified Density Factor-based HBA (MDF-HBA). The detected output through disc-segmented and cup-segmented images are undergone averaging to finalize the optimal detection output.