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

• Published in: IEEE access Vol. 4; pp. 7567 - 7576
• Main Authors: Shui-Hua Wang, Tian-Ming Zhan, Yi Chen, Yin Zhang, Ming Yang, Hui-Min Lu, Hai-Nan Wang, Bin Liu, Phillips, Preetha
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biorthogonal wavelet transform; computer vision; kernel principal component analysis; Kernels; Logical regression; logistic regression; machine learning; Magnetic resonance imaging; Medical diagnosis; Multiple sclerosis; Principal component analysis; Principal components analysis; Regression analysis; Statistical analysis; Wavelet analysis; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2016.2620996

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.