Journal
IEEE ACCESS
Volume 4, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2620996
Keywords
Biorthogonal wavelet transform; kernel principal component analysis; logistic regression; multiple sclerosis; computer vision; machine learning
Categories
Funding
- Natural Science Foundation of Jiangsu Province [BK20150523, BK20150983]
- NSFC [61502206, 61602250]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [16KJB520025]
- Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing [BD201607]
- Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau [SDL201608]
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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 +/- 0.14%, 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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