Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

Xing Xing Zhou*, Jian Fei Yang, Hui Sheng, Ling Wei, Jie Yan, Ping Sun, Shui Hua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

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%.

Original languageEnglish
Pages (from-to)827-837
Number of pages11
JournalSIMULATION
Volume92
Issue number9
DOIs
Publication statusPublished - 1 Sept 2016
Externally publishedYes

Keywords

  • Magnetic Resonance Imaging
  • Principal Component Analysis
  • kernel support vector machine
  • stationary wavelet transform
  • support vector machine

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