Brain tumor segmentation using genetic algorithm and ANN techniques

摘要

The Research of experiment is to develop an interaction CAD system as helpful medical in multiple brain tumor classify. The Research is performance on a diversified dataset of 350 post con trast T1-weighted MR images of 30 patients and publically available dataset of 280 post contrastT1-weighted MR images of 30 patients. The first dataset includes main brain tumors such as Astrocystoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medullobla stoma (MED) and Meningioma(MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the research. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy.