Glaucoma detection based on deep convolutional neural network.


Conference Paper

Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

Full Text

Duke Authors

Cited Authors

  • Xiangyu Chen, ; Yanwu Xu, ; Damon Wing Kee Wong, ; Tien Yin Wong, ; Jiang Liu,

Published Date

  • August 2015

Published In

Volume / Issue

  • 2015 /

Start / End Page

  • 715 - 718

PubMed ID

  • 26736362

Pubmed Central ID

  • 26736362

International Standard Serial Number (ISSN)

  • 1557-170X

Digital Object Identifier (DOI)

  • 10.1109/EMBC.2015.7318462

Conference Location

  • United States