AI-Powered Mobile Application for Visually Impaired People

Abstract

Automatic recognition of diabetic retinal degeneration using machine learning and fundus images involves leveraging computational techniques to detect and diagnose diabetic retinopathy, a common complication of diabetes affecting the retina. Fundus images, which capture the back of the eye, are analyzed through machine learning algorithms to identify characteristic signs of retinal degeneration indicative of diabetic retinopathy. This process typically involves preprocessing of images to enhance features, followed by feature extraction and selection to highlight relevant patterns. Machine learning models, such as convolutional neural networks (CNNs) or support vector machines (SVMs), are then trained on labeled datasets to classify images into different stages of retinopathy severity. By automating this recognition process, early detection and intervention can be facilitated, potentially reducing the risk of vision loss in diabetic patients.