AUTOMATIC DETECTION OF MACULAR EDEMA WITH ITS SEVERITY ANALYSIS

Abstract

The Macula is an important part of our human visual system that is primarily responsible for sharp and colour vision. Diabetes affects many different parts of the body, including the retina of the eye. Edema and other abnormalities near the macula are caused by retinal damage. Diabetes-related macular edema (DME) is a group of disorders that affect the macula. It has an effect on patients' vision, which can lead to vision loss. The information regarding the severity of the disease and the localization of pathologies is extremely useful to the ophthalmologist in detecting the disease and selecting the best treatment plan for the patients in order to avoid the formation of lesions and prevent vision loss. It can be avoided if the reasons for edema are identified in advance. The enlargement is caused by neovascularization and other irregularities in the blood vessels around the macula. The objective of this work focuses on preventing vision loss by recognizing abnormalities in the macula in advance. The goal of this work is to use digital OCT (Optical Coherence Tomography) pictures to construct an automated detection of edema. Using Fuzzy k- means Machine Learning algorithms to extract and detect the afflicted region, for early discovery of edema to prevent or postpone sight loss.