E2E-DRNet:基于EfficientNetV2模型的糖尿病视网膜病变识别
E2E-DRNet:Diabetic Retinopathy Recognition with EfficientNetV2 Model
刘圆圆 1陈麓 1鲁峰 1叶阳 1安禹潼 1金明慧 1邢开原 1曾光2
作者信息
- 1. 哈尔滨医科大学(大庆)医学信息学系,大庆 163319
- 2. 大庆市油田总医院眼科,大庆 163319
- 折叠
摘要
本文提出一种名为E2E-DRNet的模型,旨在解决当前人工糖尿病视网膜病变(diabetic retinopathy,DR)诊断中分类性能差、耗时费力以及视网膜图像等级差异小、病灶不明显等问题.该模型基于EfficientNetV2,并结合了有效通道注意力模块.通过对DR数据集进行处理和优化,引入了Focal Loss损失函数以解决样本不均衡问题,并将模型分为两个阶段以实现DR分类的细分.实验结果表明,该方法在公开数据集和临床数据集上表现良好,提高了对眼底病变区域的可解释性,有助于提高DR病变的筛查效率,克服了人工诊断的局限性.
Abstract
This study proposes a model called E2E-DRNet to address issues in manual diabetic retinopathy(DR)diagnosis,including poor classification performance,laborious processes,minimal differences in grades of retinal images,and inconspicuous lesions.This model is based on EfficientNetV2 and incorporates the efficient channel attention(ECA)module.By processing and optimizing a DR dataset,the Focal Loss function is introduced to address sample imbalance.The model achieves refined DR classification through two stages.Experimental results demonstrate that the proposed model performs well on both public and clinical datasets.Additionally,it enhances the interpretability of lesion regions in fundus images,thereby improving the efficiency of DR lesion screening and overcoming the limitations of manual diagnosis.
关键词
糖尿病视网膜病变/EfficientNetV2/有效通道注意力/可解释性Key words
diabetic retinopathy(DR)/EfficientNetV2/efficient channel attention/interpretability引用本文复制引用
出版年
2024
NETL
NSTL
万方数据