Advancements in Fundus Image Analysis: A Comprehensive Method of AI-Based Classification and Segmentation Technique

Abstract

Medical imaging is a vital tool in the existing healthcare system for precise analysis and treatment of various medical conditions. The use of artificial intelligence particularly machine learning and deep learning approaches has changed the interpretation of medical images and resulted in substantial advances in the area. With the increasing occurrence of eye diseases and the imperative need for early diagnosis, artificial intelligence presents potential solutions for the automatic, precise, and early detection of various eye problems. New advances in machine learning and deep learning allow different ocular disorders to be automatically analyzed, classified, and segmented which leads to early and precise diagnosis. This study attempts to give a comprehensive overview of the rapidly evolving field by reviewing the most recent approaches, challenges, and possible uses of artificial intelligence in medical imaging for ocular illnesses with a focus on segmentation and classification. The research also presents advanced methodologies such as transfer learning with MobileNet and UNet for automating the diagnosis of various ocular problems using classification and segmentation. The study highlights the importance of early and accurate diagnoses for better patient outcomes.