Residual convolutional neural network for diabetic retinopathy

Syahidahizza Rufaida, Mohamad Ivan Fanany

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

2 Citations (Scopus)

Abstract

This research proposes a method to detect diabetic retinopathy automatically based on fundus photography evaluation. This automatic method will speed up diabetic retinopathy detection process especially in Indonesia which lack of ophthalmologist. Besides, the difference of doctor ability and experience may produce an inconsistent result. Thus, with this method, we hope automatic detection of diabetic retinopathy will speed up with a consistent result so blindness effect from diabetic retinopathy can be prevented as early as possible. Convolutional Neural Network (CNN) is one of neural network variant which can detect the pattern on an image very well. Residual CNN is one of CNN variant which can prevent accuracy degradation for a deep neural network. Therefore this inspire us to apply Residual CNN on diabetic retinopathy. This Residual Network can detect diabetic retinopathy with kappa score 0.51049.

Original languageEnglish
Title of host publication2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages367-373
Number of pages7
ISBN (Electronic)9781538631720
DOIs
Publication statusPublished - 4 May 2018
Event9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 - Jakarta, Indonesia
Duration: 28 Oct 201729 Oct 2017

Publication series

Name2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Volume2018-January

Conference

Conference9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Country/TerritoryIndonesia
CityJakarta
Period28/10/1729/10/17

Keywords

  • Classification
  • Differentiable Neural Computer
  • Neural Network
  • Sequence

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