1 Citation (Scopus)

Abstract

The number of glaucoma sufferers is expected to increase to around 111.8 million in 2040 due to aging and population growth. It is necessary to develop technology to detect glaucoma automatically with a high degree of accuracy and efficiency. This research made the glaucoma classification system by comparing CNN architecture (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101) and implementing the CLAHE method as a pre-processing step. The evaluation system based on accuracy shows that the implementation of CLAHE can improve accuracy by about one to two percent. The average accuracy on Resnet-101 reached 88.24%, followed by average accuracy of ResNet-50, ResNet-18, AlexNet, and GoogleNet architectures with 87.82 %, 86.01%, 85.68%, and 85.63% accuracy, respectively. From all experiments, the best accuracy result is 89.59% on the ResNet-50 architecture experiment with a batch size of 16.

Original languageEnglish
Title of host publication2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6-11
Number of pages6
ISBN (Electronic)9781665481656
DOIs
Publication statusPublished - 2022
Event10th International Conference on Information and Communication Technology, ICoICT 2022 - Virtual, Online, Indonesia
Duration: 2 Aug 20223 Aug 2022

Publication series

Name2022 10th International Conference on Information and Communication Technology, ICoICT 2022

Conference

Conference10th International Conference on Information and Communication Technology, ICoICT 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period2/08/223/08/22

Keywords

  • CLAHE
  • CNN
  • Deep Learning
  • Glaucoma
  • Image Enhancement

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