glaucoma detection and classification model for retinal oct images in the framework of deep learning techniques

Abstract

newline Irreversible vision loss is a common consequence of glaucoma, demands accurate and newlinetimely diagnosis for effective Glaucoma Detection (GD). This research aims to enhance newlineglaucoma classification accuracy by fusing information from two distinct imaging modalities newlinelike Optical Coherence Tomography Images (OCTIs) and fundus images (FIs). The research newlinework is divided into three stages, segmentation of OCTIs and FIs, feature based fusion of newlineOCTIs and FIs using Deep Learning (DL) and Multi-Modal Convolution Neural Networks newline(MM-CNN) approach using fusion of FIs and OCTIs. newlineSegmentation of both FIs and OCTIs are essential to improve the GD system newlineperformance in medical imaging processing, it serves as a crucial role in both diagnosing and newlineenhancement of images. Segmentation of FIs involves dividing retinal images acquired via newlinefundus photography into separate regions like inner surface of the eye, encompasses several newlineanatomical elements such as the optic disc, macula and blood vessels. Utilizing an active newlinecontour model along with a matched filter and the Hessian matrix for segmenting FIs offers newlinenumerous advantages for GD. The OCTIs layer segmentation encompasses the delineation of newlinevarious retinal and ocular tissue layers using OCT imaging technology. It a non-invasive newlineimaging technique, delivers detailed, high-resolution cross-sectional images of the retina, newlineenabling clinicians to visualize micro-structural changes effectively. The segmentation process newlineinvolves identifying and outlining layers such as the retinal nerve fiber layer, ganglion cell newlinelayer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, newlinephotoreceptor layer, retinal pigment epithelium, and choroid. Precise segmentation of these newlinelayers is vital for quantifying thickness, volume and other morphological parameters, thereby newlineassisting in the diagnosis, monitoring, and treatment of glaucoma diseases. newlineUsing deep learning explores the fusion of these modalities through an innovative neural newlinenetwork architecture with op