期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：1
页码：463-472
DOI：10.12928/telkomnika.v17i1.11604
出版社：Universitas Ahmad Dahlan
摘要：Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can
appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic
Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image
classification system between normal and neovascularization is here presented. The classification using
Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine,
k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no
data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on
the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and
Retina Image Bank. The patches are made from a manual crop on the image that has been marked by
experts as neovascularization. The dataset consists of 100 data patches. The test results using three
scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%.
The test performs using a single Graphical Processing Unit (GPU).
其他摘要：Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU).
