屈莹,陈晨,吕小毅

• 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

摘要(Abstract)：

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.

关键词(KeyWords)： 葡萄膜炎;眼底OCT图像;机器学习;ROC曲线;辅助诊断

Abstract:

Keywords:

基金项目(Foundation): 天山青年-杰出青年科技项目(2019Q003)

作者(Author): 屈莹,陈晨,吕小毅

DOI: 10.13568/j.cnki.651094.651316.2020.11.16.0003

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