The purpose of this study is to classify glaucoma and non-glaucoma images from REFUGE dataset of fundus images. Due to the imbalance of dataset, we did data augmentation and preprocessing for dataset first (including feature extraction and enhancement). We then tested the performance of some deep convolutional neural networks as baselines, including ResNet, GoogLeNet, and VGGNet. Later we introduced self-attention layer into our CNN model and tried a method based on cup-to-disc ratio. Compared to the unprocessed dataset, the processed (data augmentation and feature enhancement) dataset gave a better performace. And self-attention model also improved performance beyond original CNN. Finally our method base on the cup-to-disc ratio was way better than the CNN models above.

中文翻译：

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青光眼分类深度网络的比较研究

本研究的目的是对眼底图像 REFUGE 数据集中的青光眼和非青光眼图像进行分类。由于数据集不平衡，我们首先对数据集进行数据增强和预处理（包括特征提取和增强）。然后，我们测试了一些深度卷积神经网络的性能作为基准，包括 ResNet、GoogLeNet 和 VGGNet。后来我们将自注意力层引入到我们的 CNN 模型中，并尝试了一种基于杯盘比的方法。与未处理的数据集相比，处理后的（数据增强和特征增强）数据集具有更好的性能。而且自注意力模型也比原来的CNN提升了性能。最后，我们基于杯盘比的方法比上面的 CNN 模型要好得多。