期刊论文

中文

电子测量技术

1002-7300

2024

47

03

127-134

10.19651/j.cnki.emt.2314726

本校为第一机构

精准的糖尿病视网膜病变的分割是实现视网膜病变自动诊断的前提条件和关键步骤,然而现有大部分的分割模型存在着参数量大、模型训练效果不理想、甚至是无法正常处理数据集等局限性.为此,在原 U-Net 网络中加入改进的 Ghost卷积模块与多尺度特征融合模块,提出一种改进 U-Net眼底病变分割图像的算法.该模型能以少量的参数量、较低的计算复杂度获得良好的分割结果.利用 Ghost Model替换原始卷积,设计出 Ghost卷积与 Ghost下采样卷积模块,在保证准确度的同时降低参数量;设计出一种轻量级的 Half-UNet多尺度特征融合模块来获取多尺度信息,针对不同尺度病变目标,引入CBAM注意力机制以改善其适应性,从而...

Accurate segmentation of diabetes retinopathy is the prerequisite and key step to achieve automatic diagnosis of retinopathy.However,most of the existing segmentation models have limitations such as large parameters,unsatisfactory model training effect,and even inability to process data sets normally.To this end,an improved Ghost convolution module and multi-scale feature fusion module are added to the original U-Net network,and an improved U-Net algorithm for fundus lesion segmentation images is proposed.This model can achieve good segmentation results with a small number of parameters and lo...