Abstract
Glaucoma is a fatal, worldwide disease that can cause blindness after cataracts for people over 40-60 years. Statistics on glaucoma have shown that around 65 million people worldwide affect by glaucoma, and it is the second major reason for vision impairment after cataract. This study uses three different Convolutional Neural Networks (CNNs) architectures, namely Inception-v3, Visual Geometry Group 19 (VGG19), Residual Neural Network 50 (ResNet50), to classify glaucoma subjects using eye fundus images. In addition, several data pre-processing and augmentation techniques were used to avoid overfitting and achieve high accuracy. The aim of this paper is to comparative analysis of the performance obtained from different configurations with CNN architectures and hyper-parameter tuning. Among the considered deep learning models, the Inception-v3 model showed the highest accuracy of 98.52% for the ACRIMA fundus image dataset.
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