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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\u00d76-fold stratified cross-validation (SCV) for Dataset-66, and 5\u00d75-fold SCV for the other two datasets show that the average accuracy is almost 100.00%.<\/jats:p>","DOI":"10.1177\/0037549716629227","type":"journal-article","created":{"date-parts":[[2016,3,2]],"date-time":"2016-03-02T23:34:05Z","timestamp":1456961645000},"page":"827-837","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":35,"title":["Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection"],"prefix":"10.1177","volume":"92","author":[{"given":"Xing-Xing","family":"Zhou","sequence":"first","affiliation":[{"name":"Nanjing Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian-Fei","family":"Yang","sequence":"additional","affiliation":[{"name":"Nanjing Normal University, 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