Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet outperforms some state-of-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.

糖尿病视网膜病变 (DR) 是糖尿病最常见的眼部并发症，也是导致失明和视力障碍的主要原因之一。自动化准确的DR分级对于眼底疾病的及时有效治疗具有重要意义。当前的临床方法仍然受到潜在的时间消耗和高风险的影响。在本文中，提出了一种分层粗到细网络 (CF-DRNet) 作为一种自动临床工具，使用卷积神经网络 (CNN) 对五个阶段的 DR 严重性等级进行分类。CF-DRNet 符合 DR 分级的层次特性，有效提升了五级 DR 分级的分类性能，包括以下内容： (1) Coarse Network 进行 No DR 和 DR 两类分类，其中注意门模块突出显着病变特征并抑制不相关的背景信息。(2) Fine Network 提出了从 Coarse Network 中划分出 DR 级别的 DR 严重度等级的四个阶段，包括轻度、中度、重度非增殖性 DR (NPDR) 和增殖性 DR (PDR)。实验结果表明，所提出的 CF-DRNet 在公开可用的 IDRiD 和 Kaggle 眼底图像数据集中优于一些最先进的方法。这些结果表明我们的方法能够在临床中实现高效可靠的 DR 分级诊断。实验结果表明，所提出的 CF-DRNet 在公开可用的 IDRiD 和 Kaggle 眼底图像数据集中优于一些最先进的方法。这些结果表明我们的方法能够在临床中实现高效可靠的 DR 分级诊断。实验结果表明，所提出的 CF-DRNet 在公开可用的 IDRiD 和 Kaggle 眼底图像数据集中优于一些最先进的方法。这些结果表明我们的方法能够在临床中实现高效可靠的 DR 分级诊断。