Paper Contents
Abstract
Age-related Macular Degeneration (AMD) is an eye disease that damages the macula, causing central vision loss, especially in older adults. AMD is a major cause of central vision loss in people over 50, and early detection can help slow development and preserve vision through medical treatments, lifestyle modifications, or treatments. For this reason, it is imperative to focus on diagnosing AMD. Effective management of AMD depends on early fundus imaging detection and classification. The state-of-the-art methods for AMD segmentation from fundus images are reviewed in this study, with an emphasis on both current Deep Learning (DL) algorithms and conventional image processing methods. It offers a comprehensive evaluation of different segmentation methodologies and their applicability in medical applications. The obstacles, limitations, and future paths in this quickly developing sector are also covered.
Copyright
Copyright © 2025 J Jency. This is an open access article distributed under the Creative Commons Attribution License.
