Detection of Diabetic Retinopathy Using Principal Component Analysis and Deep Neural Networks
- Author(s): J. Jayashree, J. Vijayashree, and N.Ch.S.N. Iyengar
PAPER DETAILS
- Computer Science and Engineering
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Paper ID: UIJRTV2I120015
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Volume: 02
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Issue: 12
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Pages: 114-121
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October 2021
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ISSN: 2582-6832
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CITE THIS
J. Jayashree, J. Vijayashree, and N.Ch.S.N. Iyengar, 2021. Detection of Diabetic Retinopathy Using Principal Component Analysis and Deep Neural Networks. United International Journal for Research & Technology (UIJRT), 2(12), pp.114-121.
Abstract
Diabetic Retinopathy (DR) is a common problem of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not identified early, it can lead to blindness. Early detection of DR and treatment can significantly reduce the risk of vision loss. In this paper, Principal component analysis technique is used for selecting the best features and deep neural network is used for classifying the presence and absence of DR.
Keywords: Diabetic Retinopathy, optimization, feature selection.
