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

IEEE Access

Volumn 4, Issue , 2016, Pages 7567-7576

  Wang, Shui Hua ^a,b,c   Zhan, Tian Ming ^a   Chen, Yi ^a,b   Zhang, Yin ^d   Yang, Ming ^e   Lu, Hui Min ^f   Wang, Hai Nan ^b   Liu, Bin ^g   Phillips, Preetha ^h,i  

^a NANJING AUDIT UNIVERSITY   (China)

^b NANJING NORMAL UNIVERSITY   (China)

^c City College of New York   (United States)

^d ZHONGNAN UNIVERSITY OF ECONOMICS AND LAW   (China)

^e NANJING MEDICAL UNIVERSITY   (China)

^f KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^g AFFILIATED ZHONGDA HOSPITAL OF SOUTHEAST UNIVERSITY   (China)

^h SHEPHERD UNIVERSITY   (United States)

ⁱ WEST VIRGINIA SCHOOL OF OSTEOPATHIC MEDICINE   (United States)

Author keywords

Biorthogonal wavelet transform; computer vision; kernel principal component analysis; logistic regression; Machine learning; multiple sclerosis

Indexed keywords

COMPUTER VISION; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; REGRESSION ANALYSIS; WAVELET TRANSFORMS;

BIORTHOGONAL WAVELET TRANSFORMS; FIVE STATE; HEALTHY CONTROLS; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); LOGISTIC REGRESSIONS; MS DETECTION; MULTIPLE SCLEROSIS; RBF KERNELS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 85013218686     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2016.2620996     Document Type: Article

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