目的 针对实际场景下小样本糖尿病性视网膜病变识别模型重要特征不易表达，真、假特征系数分布过于平稳的问题，提出了一种基于改进胶囊网络的小样本糖尿病性视网膜病变识别方法。 方法 首先采取删除图像不必要边界信息并使用基于Haar基函数的离散小波变换来处理图像的方法，来提高图像的特征表达能力，凸显重要病灶特征；其次，对胶囊网络的卷积层进行改进，采用多分支结构提取视网膜图像的多尺度特征，并添加卷积块注意力模块（convolutional block attention module，CBAM），封装并送入胶囊层中；最后，在动态路由中用sigmoid函数替代softmax函数，增强了模型的鲁棒性。 结果 所改进网络模型在节选并处理后的Kaggle公开数据集中测试准确率为98.62%。 结论 改进胶囊网络在小样本糖尿病性视网膜病变识别的任务中所取得的精确度高于当前其他先进算法。

Objective To address the challenges of accurately capturing critical features in small?sample diabetic retinopathy （DR） recognition models in real?world applications， and the overly smooth distribution of true and false feature coefficients， we propose an enhanced small?sample DR recognition method based on an improved capsule network. Methods Firstly， the method enhances image feature representation by removing redundant boundary information and employing discrete wavelet transform based on the Haar wavelet function， thereby highlighting critical pathological features. Secondly， the convolutional layer of the capsule network is optimized through a multi?branch architecture to extract multi?scale features from retinal images， while incorporating a convolutional block attention module that is subsequently fed into the capsule layer. Finally， the sigmoid function replaces the softmax function in dynamic routing， thereby improving the model's robustness. Result The enhanced neural network model achieved an accuracy of 98.62% on the Kaggle public dataset following a rigorous selection and preprocessing procedure. Conclusion The enhanced capsule network demonstrated superior precision in identifying diabetic retinopathy within small sample sizes compared to other state?of?the?art algorithms currently available.