Digital images are obtained from the retina and graded by trained professionals. Progression of diabetic retinopathy is assessed by its severity, which in turn determines the frequency of examinations. However, a significant shortage of professional observers has prompted computer assisted monitoring. The condition of the vascular network of human eye is an important diagnostic factor in retinopathy. A condition that affects eye vision is called glaucoma. This sickness is recognized as the irreversible condition that results in the vision deterioration. Many models for deep learning (DL) have been established for the appropriate detection of glaucoma so thus far. So this study gives architecture for the proper deep learning-based glaucoma detection by utilizing the CNN (convolutional neural network).The Inter disciplinary project proposes the Retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. It plays important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal. The aim of this interdisciplinary paper is to develop an artificial intelligence model that accurately classifies whether a patient is affected by glaucoma using fundus eye images. This will be achieved through deep learning techniques, specifically leveraging convolutional neural networks (CNNs) to analyze the intricate patterns and features in retinal images.
Glaucoma Detection, Deep Learning, CNN (Convolutional Neural Network), Artificial Neural Network (ANN)