[1]王文静,张莉钏,王欣,等.融合改进Retinex图像增强与深度学习的糖尿病视网膜分类检测方法[J].中国医学物理学杂志,2024,41(9):1086-1092.[doi:DOI:10.3969/j.issn.1005-202X.2024.09.005]
 WANG Wenjing,ZHANG Lichuan,WANG Xin,et al.Classification and detection method for diabetic retinopathy based on the combination of improved Retinex image enhancement and deep learning[J].Chinese Journal of Medical Physics,2024,41(9):1086-1092.[doi:DOI:10.3969/j.issn.1005-202X.2024.09.005]
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融合改进Retinex图像增强与深度学习的糖尿病视网膜分类检测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第9期
页码:
1086-1092
栏目:
医学影像物理
出版日期:
2024-10-25

文章信息/Info

Title:
Classification and detection method for diabetic retinopathy based on the combination of improved Retinex image enhancement and deep learning
文章编号:
1005-202X(2024)09-1086-07
作者:
王文静1张莉钏1王欣1刘玉红12
1.成都医学院生物医学工程教研室, 四川 成都 610500; 2.电子科技大学信息与软件工程学院, 四川 成都 610054
Author(s):
WANG Wenjing1 ZHANG Lichuan1 WANG Xin1 LIU Yuhong1 2
1. Department of Biomedical Engineering, Chengdu Medical College, Chengdu 610500, China 2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
关键词:
深度学习图像分类图像增强糖尿病视网膜病变
Keywords:
Keywords: deep learning image classification image enhancement diabetic retinopathy
分类号:
R318;TP751
DOI:
DOI:10.3969/j.issn.1005-202X.2024.09.005
文献标志码:
A
摘要:
目的:提出一种基于图像增强算法和深度学习的糖尿病视网膜分类检测方法,对糖尿病视网膜病变图像进行自动分类,实现对眼底病变程度的等级划分。方法:采用一种经过改进的Retinex图像增强算法,对原始图像进行预处理操作,从而显著提高图像质量,有效增强图像的视觉效果,使其更具清晰度和对比度。并结合深度学习方法,对不同时期的病变程度进行自动分类检测。结果:本文方法在提高分类准确率、灵敏度和特异性方面具有显著优势。与传统Retinex方法相比,本文方法的准确率、灵敏度和特异性分别提高5.4%、7.4%和16.6%。结论:利用本文方法可以有效实现糖尿病视网膜病变的自动分类和检测,从而提高其准确性和效率。
Abstract:
Abstract: Objective To present a novel method based on the image enhancement algorithm and deep learning for automatically classifying diabetic retinopathy images, and realizing the graded classification of fundus lesions. Methods An improved Retinex image enhancement algorithm was employed to preprocess the original images for significantly improving image quality and visual effect, and enhancing image clarity and contrast. Then, deep learning method was used to automatically detect and classify the degree of lesions in different periods. Results The proposed method was advantageous in improving classification accuracy, sensitivity, and specificity which were 5.4%, 7.4%, and 16.6% higher than those of traditional Retinex method. Conclusion The proposed method can effectively realize the automatic detection and classification of diabetic retinopathy, which is helpful to enhance diagnostic accuracy and efficiency.

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备注/Memo

备注/Memo:
【收稿日期】2024-02-23 【基金项目】国家自然科学基金(82001906, 82173753);四川省自然科学基金(2021YJ0138);四川省级创新创业训练计划项目(S202313705079, S202213705102) 【作者简介】王文静,研究方向:图像处理,E-mail: 3030645509@qq.com 【通信作者】刘玉红,博士,教授,研究方向:医学图像及信号处理,E-mail: amberliu@cmc.edu.cn
更新日期/Last Update: 2024-09-26