Open AccessGlaucoma Detection using SVM Classifier
Author(s) -
G Neha,
Leena Rajani,
B. Santosh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g4879.059720
Subject(s) - glaucoma , artificial intelligence , support vector machine , computer science , pattern recognition (psychology) , optic disc , principal component analysis , segmentation , optic cup (embryology) , blindness , feature extraction , classifier (uml) , fundus (uterus) , computer vision , optometry , ophthalmology , medicine , biochemistry , chemistry , gene , eye development , phenotype
Glaucoma is a autistic eye disease and major causes of firm blindness worldwide. For this we are trying to design a tool for early detection of glaucoma. In this paper glaucoma detection is based on the algorithm of retinal fundus images[1]. A supervised techniques for the detection of glaucoma is used. For the extraction of the features of the images we used PCA(principal component analysis). And for the classification support vectors are used. It shows mainly an artificial intelligent system for the segmentation of optic disk and cup. The accuracy of this model is comparatively much more greater than previously designed neural architectures.