Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression

Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression

To detect multiple sclerosis (MS) diseases early, we proposed a novel method on the hardware of magnetic resonance imaging, and on the software of three successful methods: biorthogonal wavelet transform, kernel principal component analysis, and logistic regression. The materials were 676 MR slices...

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Bibliographic Details
Main Authors: Shui-Hua Wang, Tian-Ming Zhan, Yi Chen, Yin Zhang, Ming Yang, Hui-Min Lu, Hai-Nan Wang, Bin Liu, Preetha Phillips
Format: Article
Language:English
Published: IEEE 2016-01-01
Series:IEEE Access
Subjects:
Biorthogonal wavelet transform
kernel principal component analysis
logistic regression
multiple sclerosis
computer vision
machine learning
Online Access:https://ieeexplore.ieee.org/document/7747672/

Internet

https://ieeexplore.ieee.org/document/7747672/

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