Graph-based saliency and ensembles of convolutional neural networks for glaucoma detection

Glaucoma, after cataracts, is the second leading cause of worldwide vision loss. An ophthalmologist may use various tools and methods to diagnose a glaucomatous eye. Computer-aided methods involving deep convolutional neural networks also made it recently possible to detect glaucoma on fundus images. Previous studies traditionally trained a single convolutional neural network for automatic detection of glaucoma. In this study, a more advanced way of accurate automated glaucoma recognition is proposed. First, a graph-based saliency region detection is used to crop the optic disc and remove the redundant parts of the fundus images. Then, four methods are used to ensemble convolutional neural network models comprising up to three deep learning architectures for glaucoma classification. The detection performance of this model is better than a recent study that used the same dataset. It is also as good as, or better than, the results reported by other recent research in the literature on glaucoma detection.