A Unique Six Sigma Based Segmentation Technique for Brain Tumor Detection and Classification using Hybrid CNN-SVM Model

Abstract

An intelligent organizing scheme to detect and classify normal, abnormal MRI brain sequences has been illustrated here. At present, handling of brain tumors disease and decision is based on radiological appearance and its symptoms. Magnetic-Resonance-Imaging (MRI) is a powerful substantial precise instrument for functional conclusion of brain tumorous. In existing study, broad range of methods is used for brain cancer detection and classification. Under this methods viz., image pre-processing, enhancement, segmentation, feature mining and resulting classification is efficiently conducted. Furthermore, when various machine learning algorithms like: Six Sigma, Convolutional Neural Network (CNN), Support Vector Machine (SVM), are employed to detect and extract the tumor region and classify numerous sequence of imageries, it is witnessed from our results that this Hybrid CNN-SVM model gives maximum classification accuracy rate of 99.33% compared to previous models. The foremost aim of this research is to get an effective result for detecting type of brain tumor using six sigma based segmentation technique, and to achieve efficient classification rate, using hybrid CNN-SVM model.