Evaluation and Classification of the Brain Tumor MRI using Machine Learning Technique
The proposed work implements a Machine Learning Technique (MLT) to evaluate and classify the tumor regions into low/high grade based on the analysis carriedout with the brain MRI slices. The MLT implements a sequence of procedures, such as pre processing, post processing and classification procedures. The pre processing enhances the tumor section based on Social Group Optimization (SGO) algorithm assisted Fuzzy Tsallis thresholding. The robustness of the proposed thresholding is also confirmed by considering the noise corrupted MRI slices. The post processing implements the Level Set Segmentation (LSS) to mine the tumor region. The performance of the LSS is validated with segmentation procedures, like Active Contour (ACS) and Chan Vese (CVS) technique. The fundamental data of the tumor section is then extracted using the Gray Level Co occurrence Matrix (GLCM) and most dominating features are then chosen with a statistical test. Finally, a two class classifier is implemented using the Support Vector Machine with Radial Basis Function (SVM RBF) kernel and its performance is then validated with other classifiers, like the Random Forest and k Nearest Neighbor. The outcome of the proposed work confirms that, implemented tool with the SVM RBF helps to achieve an accuracy of >94% on the benchmark BRATS2015 database.