Glaucoma classification using a polynomial-driven deep learning approach

Abstract

In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image purification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGCNet has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net performance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).