School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400000, China.
Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 400000, China.
J Healthc Eng. 2022 Mar 24;2022:9585344. doi: 10.1155/2022/9585344. eCollection 2022.
Diabetic retinopathy is a main cause of blindness in diabetic patients; therefore, detection and treatment of diabetic retinopathy at an early stage has an important effect on delaying and avoiding vision loss. In this paper, we propose a feasible solution for diabetic retinopathy classification using ResNet as the backbone network. By modifying the structure of the residual blocks and improving the downsampling level, we can increase the feature information of the hidden layer feature maps. In addition, attention mechanism is utilized to enhance the feature extraction effect. The experimental results show that the proposed model can effectively detect and classify diabetic retinopathy and achieve better results than the original model.
糖尿病性视网膜病变是糖尿病患者失明的主要原因；因此，早期发现和治疗糖尿病性视网膜病变对于延缓和避免视力丧失具有重要意义。在本文中，我们提出了一种使用 ResNet 作为骨干网络的糖尿病性视网膜病变分类的可行解决方案。通过修改残差块的结构和提高下采样水平，可以增加隐藏层特征图的特征信息。此外，还利用注意力机制增强了特征提取效果。实验结果表明，所提出的模型可以有效地检测和分类糖尿病性视网膜病变，并取得比原始模型更好的结果。
