Magnetic Resonance Brain Image Classification via Stationary Wavelet Transform and Generalized Eigenvalue Proximal Support Vector Machine

Background: Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system to distinguish abnormal brains from normal brains in MRI scanning. Methods: Our proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Finally, we proposed to use two classifiers, viz., the generalized eigenvalue proximal support vector machine (GEPSVM), and GEPSVM with RBF kernel. We tested our methods on three benchmark datasets. Results: The 10 runs of K-fold cross validation result showed the proposed SWT+PCA+GEPSVM+RBF method excelled thirteen state-of-the-art methods in terms of classification accuracy. In addition, the SWT+PCA+GEPSVM+RBF method achieved accuracy of 100%, 100%, and 99.41% on Dataset-66, Dataset-160, and Dataset-255, respectively. Conclusion: We proved the effectiveness of both SWT and GEPSVM. The proposed method may be applied to clinical use.