Reconfiguration Layers of Convolutional Neural Network for Fundus Patches Classification

Wahyudi Setiawan, Moh. Imam Utoyo, Riries Rulaningtyas

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 an 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.

Keywords


convolutional neural network, fundus classification, gradient descent, reconfiguration layers, visual geometry group


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