TY - JOUR
T1 - Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection
AU - Zhou, Xing Xing
AU - Yang, Jian Fei
AU - Sheng, Hui
AU - Wei, Ling
AU - Yan, Jie
AU - Sun, Ping
AU - Wang, Shui Hua
N1 - Publisher Copyright:
© The Author(s) 2016.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - Finding an appropriate and accurate technology for early detection of disease is significantly important to research early treatments. We proposed some novel automatic classification systems based on the stationary wavelet transform (SWT) and the improved support vector machine (SVM). Magnetic Resonance Imaging (MRI) is commonly used for brain imaging as a non-invasive diagnostic tool to assist the pre-clinical diagnosis. However, MRI generates a large information set, which poses a challenge for classification. To deal with this problem we proposed a new approach, which combines SWT and Principal Component Analysis for feature extraction. In our experiments, three different datasets and four kinds of classifiers of the SVM were employed. The results over 5×6-fold stratified cross-validation (SCV) for Dataset-66, and 5×5-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.
AB - Finding an appropriate and accurate technology for early detection of disease is significantly important to research early treatments. We proposed some novel automatic classification systems based on the stationary wavelet transform (SWT) and the improved support vector machine (SVM). Magnetic Resonance Imaging (MRI) is commonly used for brain imaging as a non-invasive diagnostic tool to assist the pre-clinical diagnosis. However, MRI generates a large information set, which poses a challenge for classification. To deal with this problem we proposed a new approach, which combines SWT and Principal Component Analysis for feature extraction. In our experiments, three different datasets and four kinds of classifiers of the SVM were employed. The results over 5×6-fold stratified cross-validation (SCV) for Dataset-66, and 5×5-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.
KW - Magnetic Resonance Imaging
KW - Principal Component Analysis
KW - kernel support vector machine
KW - stationary wavelet transform
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84988719691&partnerID=8YFLogxK
U2 - 10.1177/0037549716629227
DO - 10.1177/0037549716629227
M3 - Article
AN - SCOPUS:84988719691
SN - 0037-5497
VL - 92
SP - 827
EP - 837
JO - SIMULATION
JF - SIMULATION
IS - 9
ER -