Automated detection system for texture feature based classification on different image datasets using S-transform

Abstract

The objective of this study is to present a computer-aided diagnosis (CAD) system for automatic detection of brain tumors in brain magnetic resonance (MR) image data sets as we consider the brain image dataset from the different datasets. The proposed system initially pre-processes the input images using Fuzzy C-means (FCM) for image segmentation. Subsequently, it utilizes variant of S-transform namely discrete orthonormal S-transform (DOST) to extract the texture features and its dimensionality is reduced using Principal component analysis (PCA) and linear discriminant analysis (LDA). The reduced features are then supplied to the proposed Adaboost algorithm with Random Forest (ADBRF) classifier where the random forest is used as the base classifier for classifying the abnormal brain tumors in MRI image datasets. The simulation results based on the five runs of k-fold stratified cross-validation indicate that the proposed method yields superior accuracy (98.26%) as compared to existing schemes.