Cumulant Features based Classification of Brain MR Images using ANN and LS-SVM Algorithm

Abstract

Automatic classification of magnetic resonance (MR) brain images using machine learning algorithms has a significant role in clinical diagnosis of brain tumour. The higher order spectra cumulant features are powerful and competent tool for automatic classification. The study proposed an effective cumulant-based features to predict the severity of brain tumour. The study at first stage implicates the one-level classification of 2-D discrete wavelet transform (DWT) of taken brain MR image. The cumulants of every sub-bands are then determined to calculate the primary feature vector. Linear discriminant analysis is adopted to extract the discriminative features derived from the primary ones. A three layer feed-forward artificial neural network (ANN) and least square based support vector machine (LS-SVM) algorithms are considered to compute that the brain MR image is either belongs to normal or to one of seven other diseases (eight-class scenario). Furthermore, in one more classification problem, the input MR image is categorized as normal or abnormal (two-class scenario). The correct classification rate (CCR) of LS-SVM is superior than the ANN algorithm thereby the proposed study with LS-SVM attains higher accuracy rate in both classification scenarios of MR images.