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
Classification and detection method for diabetic retinopathy based on the combination of improved Retinex image enhancement and deep learning
王文静 1张莉钏 1王欣 1刘玉红2
作者信息
- 1. 成都医学院生物医学工程教研室,四川成都 610500
- 2. 成都医学院生物医学工程教研室,四川成都 610500;电子科技大学信息与软件工程学院,四川成都 610054
- 折叠
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
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.
关键词
深度学习/图像分类/图像增强/糖尿病视网膜病变Key words
deep learning/image classification/image enhancement/diabetic retinopathy引用本文复制引用
出版年
2024
NETL
NSTL
万方数据