Gauss-Newton Representation Based Algorithm for Magnetic Resonance Brain Image Classification

Brain tumor is a harmful disease worldwide. Every year, a majority of adults as well as children dies due to brain tumor. Early detection of the tumor can enhance the survival rate. Many brain image classification schemes are reported in the literature for early detection of tumors. Thus, it has become a challenging problem in the field of medical image analysis. In this paper, a novel hybrid method is proposed that uses the Gauss-Newton representation based algorithm (GNRBA) with feature selection approach. The proposed method is threefold. Firstly, discrete wavelet transform (DWT) is used as a pre-processing step to extract the features from the brain images. Secondly, principal component analysis (PCA) is used to address the dimensionality problem. Finally, the extracted features in the lower dimensional space are utilized by GNRBA for classification. To show the robustness of the proposed method, real human brain magnetic resonance (MR) images are used to experiment. It is witnessed from the results that the performance of the proposed method is superior as compared to the existing brain image classification methods.