糖尿病视网膜病变（DR）是糖尿病的常见并发症，是目前世界范围内导致失明的主要疾病之一。临床的早期阶段很难检测到DR。本文提出一种基于卷积神经网络的计算机辅助诊断方法，根据眼底的图像自动分类DR的严重程度。采用多种预处理方法提高输入图像的质量，并且采用多种数据增强的方法来提高数据集的均衡性。使用代价敏感正则化扩展标准分类损失函数，根据预测等级和真实等级相差程度的不同，对其施加不同的惩罚。在ImageNet数据集上进行预训练，从而引入迁移学习，并且使用Softmax激活函数的全连接层使模型获得更好的性能。基于两个数据集的实验结果表明，相较于近期学者的研究结果，该模型能够实现二次加权kappa分数约5%的改善，AUC约3%的改善。将代价敏感正则化引入到EfficientNet网络模型可以提高糖尿病视网膜病变分类任务的准确率，能够得到很好的模型性能。

Diabetic retinopathy (DR) is a common complication of diabetes and one of the major diseases leading to blindness in the world. It is difficult to detect DR in the early clinical stage. A computer aided diagnosis method based on convolution neural network is proposed, which can automatically classify the severity of DR according to the fundus images. Various preprocessing methods are used to improve the quality of the input images and various data enhancement methods are used to improve the balance of datasets. Cost-sensitive regularization is used to expand the standard classification loss function based on EfficientNet network architecture. Depending on the degree of difference between the predicted label and the ground truth label, they are applied different penalties. In addition, pre-training on ImageNet dataset is carried on to introduce transfer learning, and the full connection layer of Softmax activation functions are used to achieve better performance of the model. According to the experimental results of two datasets, compared with the recent research results, the model can achieve an improvement of about 5% in quadratic weighted kappa score and 3% in AUC. The introduction of cost-sensitive regularization into the EfficientNet network model can improve the accuracy of diabetic retinopathy classification task and obtain a good model performance.