AI-Driven Methods Cutting-Edge Deep Learning Techniques for Eye Fundus Disease Detection and Segmentation

Eye fundus diseases are critical conditions that can lead to severe vision impairment and even permanent blindness if not diagnosed and treated promptly. Manual diagnosis of these diseases is time-consuming and heavily reliant on the expertise of ophthalmologists. This proposed methodology research aims to develop an efficient and accurate diagnostic system for eye fundus disease classification and segmentation using Deep Learning techniques such as (SVM), (ANN), (CNN) and (U-Net) Model. The study involves the compilation of a comprehensive dataset of eye fundus images, encompassing various types of diseases, including diabetic retinopathy, age-related macular degeneration, glaucoma, and others. Each image is accompanied by corresponding ground-truth annotations provided by expert ophthalmologists for segmentation. The experimental results demonstrate the effectiveness and reliability of the proposed system in accurately classifying eye fundus diseases and segmenting affected regions within the images. The AI-Deep Learning algorithms achieve high accuracy and provide valuable insights into the presence and extent of various fundus diseases as the proposed methodology of SVM is 85%, ANN is 88%, CNN 93% and U-Net is 97%.