Diabetic retinopathy (DR) detection has attracted much attention recently, and the deep learning algorithms have gained traction in this area. At present, DR screening by deep learning algorithms is often based on single-view fundus images, which usually leads to an unsatisfactory accuracy of DR grading due to the incomplete lesion features. In this paper, we proposed a novel diabetic retinopathy detection convolutional network for automatic DR detection by integrating multi-view fundus images. Compared to existing single-view DCNN-based DR detection methods, the proposed method has the following advantages. First, our method fully utilizes the lesion features from the retina with a field-of-view around ${120}^{\circ }-{150}^{\circ }$. Second, by introducing the attention mechanisms, more attention will be paid on the influential view and the performance can be improved. Besides, we also assign large weights to important channels in the network for effective feature extraction. Experiments are conducted on our collected multi-view DR dataset contained 15,468 images, in which each eye sample provides four-view images. The experimental results indicate that using multi-view images is suitable for automatic DR detection and our proposed method is superior to other benchmarking methods.

糖尿病视网膜病变 (DR) 检测最近引起了广泛关注，深度学习算法在该领域受到了广泛关注。目前，深度学习算法的DR筛查往往基于单视图眼底图像，由于病变特征不完整，通常导致DR分级精度不理想。在本文中，我们提出了一种新的糖尿病视网膜病变检测卷积网络，通过集成多视图眼底图像进行自动 DR 检测。与现有的基于单视图 DCNN 的 DR 检测方法相比，所提出的方法具有以下优点。首先，我们的方法充分利用了视网膜的病变特征，视野范围在${120}^{\circ }-{150}^{\circ }$. 其次，通过引入注意力机制，将更多地关注有影响力的观点，并可以提高性能。此外，我们还为网络中的重要通道分配了较大的权重，以进行有效的特征提取。在我们收集的包含 15,468 张图像的多视图 DR 数据集上进行实验，其中每只眼睛样本提供四视图图像。实验结果表明，使用多视图图像适用于自动 DR 检测，我们提出的方法优于其他基准测试方法。