卷积神经网络
青光眼
计算机科学
人工智能
深度学习
辍学(神经网络)
接收机工作特性
模式识别(心理学)
代表(政治)
机器学习
眼科
医学
政治学
政治
法学
作者
Xiangyu Chen,Yanwu Xu,Damon Wing Kee Wong,Tien Yin Wong,Jiang Liu
标识
DOI:10.1109/embc.2015.7318462
摘要
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.
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