MRI Brain Tumor Classification Using SVM and Histogram Based Image Segmentation

A brain tumor arises due to an abnormal growth of cells that have proliferated in an uncontrolled manner. When normal cells grow old or get injured, they either undergo cell death or get repaired by own. Research shows that people affected by brain tumors die due to their inaccurate detection. In this paper, proposed an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical images like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. To avoid manual errors, an automated intelligent classification technique is proposed which caters the need for classification of image. In this paper work, classification techniques based on Support Vector Machines (SVM) and histogram based image segmentation are proposed and applied to brain image classification. Here feature extraction from MRI Images will be carried out by gray scale, symmetrical and texture features. This intelligent system improves accuracy rate and reduces error rate of MRI brain tumor classification using SVM.