Automated Glaucoma Detection via Deep Learning Approaches

Abstract

One of the main causes of irreversible blindness, glaucoma, is a progressive eye condition that sometimes goes undiagnosed until considerable vision loss takes place. For management to be effective, early detection and prompt action are essential. In recent years, deep learning—an artificial intelligence technique—has emerged as a useful tool in medical imaging, opening the door to automated glaucoma diagnosis. This work aims to detect glaucoma by using deep learning algorithms in conjunction with retinal fundus pictures.Convolutional neural networks are leveraged in the suggested system to Analyze the anatomical features.Concerning the optic papilla and retinal layers, that are important markers of glaucomatous alterations. Strong feature extraction and classification skills are attained by the system through training the model on a varied dataset of labeled fundus images. Methods like data transfer and augmentation are applied to overcome issues like as inconsistent image quality and a lack of labelled data .In order to ensure a thorough evaluation, the model’s performance is assessed in terms of accuracy,responsiveness, precision, and the under-curve area of the ROC curve.