Pathological brain detection using curvelet features and least squares SVM

This paper aims at developing an automatic pathological brain detection system (PBDS) to assist radiologists in identifying brain diseases correctly in less time. Magnetic resonance imaging (MRI) has the potential to provide better information about the brain soft tissues and hence MR images have been incorporated in the proposed system. Fifty largest coefficients are selected from each sub-band of a level-5 fast discrete curvelet transform (FDCT) to serve as a feature set for each image. To reduce the size of the feature set, principal component analysis (PCA) has been harnessed. Subsequently, least squares SVM (LS-SVM) with three different kernels are utilized to segregate the images as healthy or pathological. The proposed system has been validated on three benchmark datasets and a 10 xk-fold stratified cross validation (SCV) test has been performed. It indicates that the proposed system "FDCT + PCA + LS-SVM + RBF" achieves better performance than not only two other systems having linear and polynomial kernel but also 22 existing methods. In addition, the suggested system requires only six features which are computationally economical for a practical use.