Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method’s effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR.

中文翻译：

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基于多视角联合学习的糖尿病视网膜病变计算机辅助诊断

糖尿病视网膜病变（DR）是糖尿病的一种眼部并发症，其程度分级是患者早期诊断的重要依据。手动诊断是一个漫长且昂贵的过程，并且存在误诊的特定风险。计算机辅助诊断可以提供更准确、更实用的治疗建议。在本文中，我们提出了一种称为RT2Net的多视图联合学习DR诊断模型，该模型融合了眼底图像的全局特征和血管图像的局部细节特征，以减少单一眼底图像学习的局限性。首先，使用对比度限制自适应直方图均衡化等操作对原始图像进行预处理，并对提取的DR图像的血管结构进行分割。然后，将血管图像和眼底图像分别输入到RT2Net的两个分支网络中进行特征提取，特征融合模块自适应地融合分支网络输出的特征向量。最后，利用优化的分类模型识别DR的五类。本文在公共数据集 EyePACS 和 APTOS 2019 上进行了大量实验，以证明该方法的有效性。 RT2Net在两个数据集上的准确率达到88.2%和85.4%，受试者工作特征曲线下面积（AUC）分别为0.98和0.96。 RT2Net对DR优异的分类能力可以显着帮助患者早期发现和治疗病变，为医生提供更可靠的诊断依据，对于诊断DR具有重要的临床价值。