Glaucoma Detection using SVM Classifier
G.Yashaswini SreeNeha1, L.Rajani2, B.Santosh3
1G.yashaswini Sree Neha*, ECE Department at KONERU Lakshmaiah Education Foundation.
2L.Rajani Pursuing B.Tech, ECE DECE Department at KONERU Lakshmaiah Education Foundation.
3B.Santosh Reddy, Persuing B.Tech Degree in ECE Department at KONERU Lakshmaiah Education Foundation.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on May 10, 2020. | PP: 336-339 | Volume-9 Issue-7, May 2020. | Retrieval Number: G4879059720/2020©BEIESP | DOI: 10.35940/ijitee.G4879.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Optic disk, Cup ratio, Svm,Pca.
Scope of the Article: Classification