An effective deep learning network for detecting and classifying glaucomatous eye

Abstract

<span lang="EN-US">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%.</span>

Automated Glaucoma Detection Techniques: an Article Review

“…The employing of the EH lter in conjunction with the FCTH, using an RF classi er, reached the highest accuracy of 80.43% and an AUROC of 0.884.Ahmed MT. et al,[29] employed DL techniques to identify open-angle glaucoma in fundus images based on three distinct architectures: VGG16, VGG19, and ResNet50. They classi ed the eyes as positive or negative for glaucoma using the Kaggle database.…”

“…Ahmed MT. et al,[29] employed DL techniques to identify open-angle glaucoma in fundus images based on three distinct architectures: VGG16, VGG19, and ResNet50. They classi ed the eyes as positive or negative for glaucoma using the Kaggle database.…”