Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. (September 2016)

• Record Type: Journal Article
• Authors: Zhou, Xing-Xing; Yang, Jian-Fei; Sheng, Hui; Wei, Ling; Yan, Jie; Sun, Ping; Wang, Shui-Hua
• Is Part Of: Simulation. Volume 92:Number 9(2016:Sep.)
• Page Start: 827
• Page End: 837
• Publication Date: 2016-09
• Subjects: Magnetic Resonance Imaging -- Principal Component Analysis -- support vector machine -- stationary wavelet transform -- kernel support vector machine; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549716629227
• Languages: English
• ISSNs: 0037-5497

Abstract

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%.