Abstract:
Glaucoma is a well-known complex disease of the optic nerve that gradually
damages eyesight due to the increase of intraocular pressure inside the eyes.
Among two types of glaucoma, open-angle glaucoma is mostly happened by
high intraocular pressure and can damage the eyes temporarily or sometimes
permanently, another one is angle-closure glaucoma. Therefore, being
diagnosed in the early stage is necessary to safe our vision. There are several
ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy
but require time and expertise. Using deep learning approaches could be a
better solution. This study focused on the recognition of open-angle affected
eyes from the fundus images using deep learning techniques. The study
evolved by applying VGG16, VGG19, and ResNet50 deep neural network
architectures for classifying glaucoma positive and negative eyes. The
experiment was executed on a public dataset collected from Kaggle;
however, every model performed better after augmenting the dataset, and the
accuracy was between 93% and 97.56%. Among the three models, VGG19
achieved the highest accuracy at 97.56%.
