基于深度学习的糖尿病视网膜病变(DR)自动分类有助于提高辅助诊断的精准性和高效性.本文通过改进的残差网络来实现对五种不同病变程度的DR分类.首先,将原残差网络第一个卷积层中的7x7卷积替换为三个小尺度的3 x 3卷积来减少网络计算量;其次,针对不同病变等级间因差异过小而导致的分类不准确问题,引入混合注意力机制来使模型更关注重要的病变特征;最后,为充分提取DR图像中所包含的病变组织形态特征,采用了跨层融合卷积的方式来代替普通的残差结构.为验证改进模型的分类有效性,将它应用于Kaggle失明检测竞赛数据集APTOS2019,实验结果表明本文的改进模型对五种不同DR病变等级的分类准确率和Kappa值分别达到97.75％和0.971 7.与一些现有模型相比,该方法在分类精度和表现上具有明显优势.
Small-scale cross-layer fusion network for classification of diabetic retinopathy
Deep learning-based automatic classification of diabetic retinopathy(DR)helps to enhance the accuracy and efficiency of auxiliary diagnosis.This paper presents an improved residual network model for classifying DR into five different severity levels.First,the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network.Second,to address the issue of inaccurate classification due to minimal differences between different severity levels,a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions.Finally,to better extract the morphological features of the lesions in DR images,cross-layer fusion convolutions were used instead of the conventional residual structure.To validate the effectiveness of the improved model,it was applied to the Kaggle Blindness Detection competition dataset APTOS2019.The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75％and a Kappa value of 0.971 7 for the five DR severity levels.Compared to some existing models,this approach shows significant advantages in classification accuracy and performance.
万方数据
维普
