Classification of Diabetic Retinopathy using shallow learning approach

S. Pansawira, A. Bustamam, D. Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Diabetic retinopathy (DR) is one of the leading causes of blindness from diabetic patients. To prevent blindness and provide an effective treatment, an early detection of DR is needed. Methods for detecting DR by manual inspections exist, but very time-consuming and tedious work. In this study, DR detection method is proposed, by using Shallow Learning approach that consists of Neural Networks, Support Vector Machine, and Random Forest. The data used to build the classifier models are DR class, Age-related Macular Degeneration (AMD) class, and Normal class. From experimental results, classification approach using Support Vector Machine yielded better results compared to Random Forest and Neural Networks. On multi-class DR, Normal, and AMD classification, Support Vector Machine method achieved 100 % accuracy and 100 % sensitivity on 75 % training data, and 94.87 % accuracy and 93.33 % sensitivity on 25 % testing data.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
EditorsTerry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420014
DOIs
Publication statusPublished - 1 Jun 2020
Event5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia
Duration: 9 Jul 201910 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2242
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
CountryIndonesia
CityDepok
Period9/07/1910/07/19

Keywords

  • Diabetic retinopathy
  • neural networks
  • random forest
  • support vector machines

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