[1]张颖,赵祺旸,郗群.基于深度融合网络研究糖尿病视网膜病变[J].中国医学物理学杂志,2025,42(3):347-355.[doi:10.3969/j.issn.1005-202X.2025.03.010]
　ZHANG Ying,ZHAO Qiyang,XI Qun.Diabetic retinopathy research based on deep converged network[J].Chinese Journal of Medical Physics,2025,42(3):347-355.[doi:10.3969/j.issn.1005-202X.2025.03.010]
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基于深度融合网络研究糖尿病视网膜病变()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第3期
页码:
347-355
栏目:
医学影像物理
出版日期:
2025-03-20

文章信息/Info

Title:
Diabetic retinopathy research based on deep converged network
文章编号:
1005-202X(2025)03-0347-09
作者:
张颖 1; 赵祺旸 1; 郗群 2
1. 甘肃中医药大学信息工程学院,甘肃 兰州 730000;2. 兰州大学第二医院信息中心,甘肃 兰州 730000
Author(s):
ZHANG Ying1;  ZHAO Qiyang1;  XI Qun2
1. School of Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China; 2. Information Center, Lanzhou University Second Hospital, Lanzhou 730000, China
关键词:
糖尿病视网膜病变; 深度学习; 深度残差收缩网络; 金字塔分割注意力模块
Keywords:
diabetic retinopathy;  deep learning;  deep residual shrinkage network;  pyramid split attention module
分类号:
R318R774.1
DOI:
10.3969/j.issn.1005-202X.2025.03.010
文献标志码:
A
摘要:
基于深度学习提出一种融合网络,旨在高效、准确地辅助诊断糖尿病性视网膜病。采用数据增强技术与生成对抗 网络相结合的手段,对 EyePACS 数据集内的眼底图像实施扩充操作,有效应对眼底图像分类不均衡的难题。使用 Inception-Resnet-V2 作为主网络,并融入深度残差收缩网络和金字塔分割注意力模块,有效地过滤掉特征学习过程中的 无关信息,聚焦病灶信息,提高网络对重要特征的抓取能力。实验结果显示该优化模型能在无需事先指明病变特征的情 况下,准确率、召回率、特异性、灵敏度以及 F1 分数达到 0.951、0.950、0.990、0.950、0.950,表明本文模型在评价指标上都有 较好的性能。
Abstract:
A converged network based on deep learning is proposed to realize the efficient and accurate diagnosis of diabetic retinopathy. Both data augmentation technology and generative adversarial network are used to augment the fundus images in EyePACS dataset for effectively addressing the problem of uneven classification of fundus images. The proposed model uses Inception-Resnet-V2 as the main network, and incorporates deep residual shrinkage network and pyramid split attention module for effectively filtering out the irrelevant information in the feature learning process and focusing on the lesion information, thereby improving the network’s ability to capture important features. Experimental results show that the optimized model achieves accuracy, recall, specificity, sensitivity, and F1 score of 0.951, 0.950, 0.990, 0.950, and 0.950, respectively, without the need to specify lesion characteristics in advance, demonstrating its superiority in evaluation indicators.

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备注/Memo

备注/Memo:
【收稿日期】2024-11-10 【基金项目】甘肃省自然科学基金(20CX9JA145);兰州市科技计划项 目(2023-4-36) 【作者简介】张颖,硕士研究生,研究方向:医学图像处理,E-mail: zhangying19981212@163.com 【通信作者】郗群,硕士生导师,正高级工程师,研究方向:医院信息化、 图像处理,E-mail: 28309495@qq.com
更新日期/Last Update: 2025-03-27