Dimensionality Reduction of Brain Tumors and Classification Using Hybrid Ensemble Classifier

B. Vijay Kumar and Parasuraman Kumar

Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by enhanced morphological filtering which removes the noise that can be formed after segmentation. Dimensionality reduction is done by using the bacterial foregoing optimization (BFO) algorithm and also number of feature space is also reduced. Automatic brain tumor stage classification is done by using probabilistic neural network (PNN), Improved Extreme Learning Machine (IELM) and Online Support Vector Machine (OSVM). This phase classifies brain images into tumor and non-tumors using hybrid ensemble based classifier. Experiments have exposed that the method was more robust to initialization, faster and accurate.

Volume 11 | 01-Special Issue

Pages: 58-71