Hybrid Feature Extraction with Ensemble Classifier for Brain Tumor Classification

Abstract

Brain tumor is one of the most severe nervous system disorders affecting the health of humans and critically it will lead to death. The most elevated disease that causes a major death rate is Glioma, i.e. a primary intracranial tumor. One of the widely used techniques in medical imaging is Magnetic Resonance Imaging (MRI) which turned out as the principle diagnosis model for the analysis of glioma and its treatment. However, the brain tumor segmentation and classification process are more complicated problems to execute. This paper intends to introduce a novel brain tumor classification model that includes four major phases: (i) Pre-processing (ii) Segmentation (iii) Brain Feature extraction (iv) Brain tumor Classification. Initially, the input image is subjected to the pre-processing phase, in which the image is pre-processed under a certain process. The pre-processed images are then subjected to the segmentation phase, which is carried out by the k-means clustering. Subsequently, the segmented images are subjected to the brain feature extraction phase, in which the features are extracted using the hybrid Principal Component Analysis (PCA)-GIST feature extraction method. Then, these features are given as the input to the classification process, where the ensemble classifier is exploited for the same. Moreover, the proposed ensemble technique includes k-Nearest Neighbor (k-NN), Optimized Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM). To precisely detect the tumor classification, the NN training is performed using Elephant Herding Optimization with mutation operations (EHOMO) Algorithm via selecting the optimal weights.