Reconfiguration layers of convolutional neural network for fundus patches classification

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Convolutional neural network (CNN) is a method of supervised deep learning. The architectures including AlexNet, VGG16, VGG19, ResNet 50, ResNet101, GoogleNet, Inception-V3, Inception ResNet-V2, and Squeezenet that have 25 to 825 layers. This study aims to simplify layers of CNN architectures and increased accuracy for fundus patches classification. Fundus patches classify two categories: normal and neovascularization. Data used for classification is MESSIDOR and Retina Image Bank that have 2,080 patches. Results show the best accuracy of 93.17% for original data and 99,33% for augmentation data using CNN 31 layers. It consists input layer, 7 convolutional layers, 7 batch normalization, 7 rectified linear unit, 6 max-pooling, fully connected layer, softmax, and output layer.

Original languageEnglish
Pages (from-to)383-389
Number of pages7
JournalBulletin of Electrical Engineering and Informatics
Volume10
Issue number1
DOIs
Publication statusPublished - Feb 2021

Keywords

  • Classification
  • Convolutional neural network
  • Fundus image
  • Gradient descent
  • Reconfiguration layers

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