Hybrid wavelet support vector classification of temporal bone abnormalities

Abstract

Support vector machine (SVM) is a machine-learning algorithm, which learns to perform the classification task through a supervised learning procedure, based on pre-classified data examples. SVM uses kernel mapping to map the non-linear data in input space to a high-dimensional feature space where the data is linearly separable. A hybrid wavelet kernel construction for support vector machine is introduced in this paper. Construction of an admissible support vector (SV) kernel using multidimensional sinc wavelet is presented. The hybrid kernels are proved to be Mercer kernel. The hybrid kernels thus constructed are used for the automated detection of temporal bone abnormalities. From high resolution computed tomography (HRCT) images features are extracted and fed to the learning machine for classification. Hybrid kernels provide better classification of the signal points in the mapped feature space. The experimental results indicate promising generalization performance with the hybrid kernels.