通信与信息技术Issue(5)：56-62,123,8.
基于分数阶PCA的眼部血管超声图像分类研究
Classification study of ocular vascular ultrasound images based on fractional order PCA
摘要
Abstract
Absrtact:In this paper,an improved BP neural network classification algorithm based on Mean Fractal Principal Component Analy-sis(MFPCA)for ocular vascular ultrasound images is proposed to address the problem of low classification accuracy and difficulty in real-time assurance of ocular vascular ultrasound images.The algorithm introduces the theory of fractional-order calculus into the calculation of the covariance matrix of Principal Component Analysis(PCA),which improves the linear dimensionality reduction and the main infor-mation retention ability of PCA.The mean value algorithm is utilized to improve the fractional order PCA algorithm to calculate the frac-tional order parameters.A classification algorithm that uses the improved Sigmoid function as the activation function of the BP network is proposed,which can greatly reduce the classification duration and the consumption of computational resources.Comparison experiments with classical algorithms show that the MFPCA ocular vascular ultrasound image classification algorithm proposed in this paper can effec-tively reduce the data dimensionality by 93.5%,increase the classification accuracy to 93.75%,and greatly improve the computational speed.关键词
眼部血管超声图像/PCA/BP神经网络/激活函数Key words
Ocular vascular ultrasound images/PCA/BP neural network/Activation function分类
数理科学引用本文复制引用
张艳珠,孙嘉滨,刘雪晴..基于分数阶PCA的眼部血管超声图像分类研究[J].通信与信息技术,2025,(5):56-62,123,8.基金项目
辽宁省教育厅高等学校基本科研项目(项目编号:LJKZ0245)装备预研重点实验室基金项目(项目编号:2021JCJQLB055006) （项目编号:LJKZ0245）
