Open AccessSeverity Analysis of Macular Edema using Random Tree Classifier
Author(s) -
Deepthi K Prasad,
L. Vibha,
K R Venugopal
Publication year - 2018
Publication title -
international journal of engineering and computer science
Language(s) - English
Resource type - Journals
ISSN - 2319-7242
DOI - 10.18535/ijecs/v7i3.02
Subject(s) - computer science , artificial intelligence , pattern recognition (psychology) , retinal , classifier (uml) , edema , optic disc , random forest , diabetic retinopathy , ophthalmology , medicine , diabetes mellitus , surgery , endocrinology
Macular edema ensues when there is abnormal pile-up of fluid and results in swelling of the macula part of the retina. It is commonly associated with diabetes. It can be diagnosed by identifying exudates in the retinal images. In the proposed work, macular the retinal image is pre-processed, enhanced and segmented using morphological operations. The optic disc and macula are segmented. Various statistical features are extracted. Optimal features are selected using Haar wavelets. The selected features are classified using Random tree classifier to detect the severity of the disease in to three stages namely, normal, mild and critical. The accuracy obtained is 98.4%.