Diabetic retinopathy is the main cause of the blindness worldwide. However, the diabetic retinopathy is hard to be detected in the initial stages, and the procedure of diagnostic can be time-consuming even for experienced-experts. The segment based learning approach has shown the benefits over learning technique for detection of diabetic retinopathy: only the annotation of image level is required get the lesions and detection of diabetic retinopathy. Anyways, the performance of existing methods are limited by the utilization of handcrafted features. This paper proposes the segment based learning approach for detection of diabetic retinopathy, which mutually learns classifiers and features from the data and gets significant development on recognizing the images of diabetic retinopathy and their inside the lesions. Specifically, the pre-trained CNN is adapted to get the segment level DRE (Diabetic retinopathy Estimation) and then Integrating all segment level of (DRM) is utilized to make the classification of diabetic retinopathy images. Lastly, an end-to-end segment based learning approach to deal with the irregular lesions of diabetic retinopathy. For detection of the diabetic retinopathy images obtain area under of ROC curve is 0.963 on the Kaggle dataset and also obtains sensitivity and specificity 96.37% and 96.37% on the higher specificity and sensitivity that outperforms much better than existing model.

糖尿病性视网膜病是全世界失明的主要原因。但是，糖尿病性视网膜病变在初期很难被发现，即使对于有经验的专家，诊断过程也可能很耗时。基于片段的学习方法已显示出优于学习技术的糖尿病性视网膜病变检测的好处：仅需要图像水平的注释即可获取病变和糖尿病性视网膜病变的检测。无论如何，现有方法的性能受到手工功能的利用的限制。本文提出了一种基于段的学习方法，用于糖尿病性视网膜病变的检测，该方法可以从数据中相互学习分类器和特征，在识别糖尿病性视网膜病变及其病变内部的图像方面取得重要进展。特别，预先训练的CNN可以获取DRE（糖尿病性视网膜病变估计）的分段水平，然后将（DRM）的所有分段水平进行积分以对糖尿病性视网膜病变图像进行分类。最后，一种基于端到端段的学习方法来应对糖尿病性视网膜病的不规则病变。对于糖尿病性视网膜病变图像的检测，在Kaggle数据集上，ROC曲线下的面积为0.963，在更高的特异性和灵敏度上优于现有模型时，其灵敏度和特异性也达到96.37％和96.37％。一种基于端到端段的学习方法来应对糖尿病性视网膜病的不规则病变。对于糖尿病性视网膜病变图像的检测，在Kaggle数据集上，ROC曲线下的面积为0.963，在更高的特异性和灵敏度上优于现有模型时，其灵敏度和特异性也达到96.37％和96.37％。一种基于端到端段的学习方法来应对糖尿病性视网膜病的不规则病变。对于糖尿病性视网膜病变图像的检测，在Kaggle数据集上，ROC曲线下的面积为0.963，在更高的特异性和灵敏度上优于现有模型时，其灵敏度和特异性也达到96.37％和96.37％。