Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine

To classify brain images into pathological or healthy is a key pre-clinical state for patients. Manual classification is tiresome, expensive, time-consuming, and irreproducible. In this study, we aimed to present an automatic computer-aided system for brain-image classification. We used 90 T2-weighted images obtained by magnetic resonance images. First, we used weighted-type fractional Fourier transform (WFRFT) to extract spectrums from each magnetic resonance image. Second, we used principal component analysis (PCA) to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine (SVM) and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5-fold cross-validation results showed that this proposed "WFRFT + PCA + generalized eigenvalue proximal SVM" yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed "WFRFT + PCA + twin SVM" and better than the proposed "WFRFT + PCA + SVM." Besides, all three proposed methods were superior to eight state-of-the-art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical.