多特征融合结合机器学习算法快速筛查葡萄膜炎

屈莹; 陈晨; 吕小毅

doi:10.13568/j.cnki.651094.651316.2020.11.16.0003

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多特征融合结合机器学习算法快速筛查葡萄膜炎

信息科学与技术 | 更新时间：2026-01-21

- 多特征融合结合机器学习算法快速筛查葡萄膜炎
- 多特征融合结合机器学习算法快速筛查葡萄膜炎
- 新疆大学学报（自然科学版中英文） 2021年38卷第4期页码：439-449
- 作者机构：
  
  1. 新疆大学信息科学与工程学院
  2. 新疆大学软件学院
  3. 新疆维吾尔自治区信号检测与处理重点实验室
- 作者简介：
- 基金信息：
  
  天山青年-杰出青年科技项目(2019Q003)
- DOI：10.13568/j.cnki.651094.651316.2020.11.16.0003
  中图分类号： R773.9;TP181;TP391.41
- 纸质出版：2021
- 稿件说明：
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[1]屈莹,陈晨,吕小毅.多特征融合结合机器学习算法快速筛查葡萄膜炎[J].新疆大学学报(自然科学版)(中英文),2021,38(04):439-449.
[1]屈莹,陈晨,吕小毅.多特征融合结合机器学习算法快速筛查葡萄膜炎[J].新疆大学学报(自然科学版)(中英文),2021,38(04):439-449. DOI： 10.13568/j.cnki.651094.651316.2020.11.16.0003.

DOI：10.13568/j.cnki.651094.651316.2020.11.16.0003.

摘要

为了利用机器学习算法快速筛查出葡萄膜炎

本文分别选取了健康人和葡萄膜炎患者的眼底OCT(Optical Coherence Tomography

OCT)图像

提取图像的形态特征、灰度差分统计特征、灰度梯度共生矩阵和小波变换等多种特征

将特征串行融合;随后用Lasso算法特征提取

用多种机器学习算法进行分类研究.结果显示:基于Medium Gaussian核函数的支持向量机(Support Vector Machine

SVM)获得了90.3%的分类准确率

其受试者工作特性曲线(Receiver Operating Characteristic curve

ROC)下的面积(Area Under Curve

AUC)为0.97

为研究中的最高准确率.本文首次将机器学习分类算法应用于葡萄膜炎患者眼底OCT图像的分类中

是对葡萄膜炎诊断的探索性研究

对葡萄膜炎的辅助诊断具有重要意义.

Abstract

In order to use machine learning algorithms to quickly screen out uveitis

this paper selects OCT(Optical Coherence Tomography

OCT)images of the fundus of healthy people and patients with uveitis

and extracts the morphological features

statistical features of gray-scale difference

gray-level gradient co-occurrence matrix and wavelet transform

etc. Finally

combine a variety of machine learning algorithms for classification research. The results show that the support vector machine based on the Medium Gaussian kernel function achieves a classification accuracy of 90.3%

and the area under the receiver operating characteristic curve is 0.97

which is the highest accuracy rate in the study. This paper applies machine learning classification algorithm to the actual classification of fundus OCT images of uveitis patients for the first time

which is of great significance for the auxiliary diagnosis of uveitis.

关键词

Keywords

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