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

Shuihua Wang | Zendy; Tianming Zhan | Zendy; Yi Chen | Zendy; Yin Zhang⋆ | Zendy; Ming Yang | Zendy; Huimin Lu | Zendy; Hainan Wang | Zendy; Bin Liu | Zendy; Preetha Phillips | Zendy

Open Access

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

Author(s) -

Shuihua Wang,

Tianming Zhan,

Yi Chen,

Yin Zhang⋆,

Ming Yang,

Huimin Lu,

Hainan Wang,

Bin Liu,

Preetha Phillips

Publication year - 2016

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2016.2620996

Subject(s) - principal component analysis , kernel principal component analysis , biorthogonal system , pattern recognition (psychology) , logistic regression , biorthogonal wavelet , kernel (algebra) , artificial intelligence , wavelet transform , principal component regression , wavelet , computer science , mathematics , kernel method , statistics , support vector machine , combinatorics

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 containing plaques from 38 MS patients, and 880 MR slices from 34 healthy controls. The statistical analysis showed our method achieved a sensitivity of 97.12±.14%, a specificity of 98.25±0.16%, and an accuracy of 97.76±0.10%. Our method is superior to five state-of-the-art approaches in MS detection.

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