Retina disease classification based on colour fundus images using convolutional neural networks

Bambang Krismono Triwijoyo, Yaya Heryadi, Lukas, Adang S. Ahmad, Boy Subirosa Sabarguna, Widodo Budiharto, Edi Abdurachman

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

6 Citations (Scopus)

Abstract

This paper explores Convolutional Neural Networks (CNN) as a classifier to recognize retinal images. The dataset used in this research is public STARE color image dataset comprises of 61 × 70.46 × S3, and 31×35 pixels. The dataset is categorized into 15 classes. The experimentation shows that the CNN model can achieve 80.93 percent.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Innovative and Creative Information Technology
Subtitle of host publicationComputational Intelligence and IoT, ICITech 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538640456
DOIs
Publication statusPublished - 16 Mar 2018
Event2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017 - Salatiga, Indonesia
Duration: 2 Nov 20174 Nov 2017

Publication series

NameProceedings - 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017
Volume2018-January

Conference

Conference2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017
Country/TerritoryIndonesia
CitySalatiga
Period2/11/174/11/17

Keywords

  • classification
  • CNN
  • deep learning
  • retina disease

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