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Enhancing Job Opportunities in Rural India Through Constrained Cognitive Learning Process: Reforming Basic Education Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Automated Detection of Diabetic Retinopathy Using Weighted Support Vector Machines

Authors : Soumyadeep Bhattacharjee, Avik Banerjee

Published in: Intelligent Systems Design and Applications

Publisher: Springer International Publishing

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Abstract

Diabetic retinopathy is a complication of the eye caused by damage to the retinal cells due to prolonged suffering from diabetes mellitus and may lead to irreversible vision impairment in middle-age adults. The proposed algorithm detects the presence of Diabetic Retinopathy (DR) by segmentation of vital morphological features like Optic Disc, Fovea, blood vessels, and abnormalities like hemorrhages, exudates and neovascularization. The images are then classified using Support Vector Machines, based on data points in a multi-dimensional feature space. The proposed method is tested on 140 images from the Messidor database, from which 75 images are used to train an SVM model and the remaining 65 are used as inputs to the classifier.

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Metadata
Title
Automated Detection of Diabetic Retinopathy Using Weighted Support Vector Machines
Authors
Soumyadeep Bhattacharjee
Avik Banerjee
Copyright Year
2018
Book
Intelligent Systems Design and Applications

Print ISBN: 978-3-319-76347-7

Electronic ISBN: 978-3-319-76348-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-76348-4_35

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