Classification on magnetic resonance imaging (Mri) brain tumour using BPNN, SVM and CNN

Abstract

In this works, the main objective is to detect the high grade gliomas (HGG) and low grade gliomas (LGG) from Magnetic Resonance Imaging (MRI) Brain Tumour images by applying the efficient image segmentation and classify among them. So hybrid image segmentation techniques applied in this work, first one is canny edge detection which is used to locate the boundary of the image and second is fuzzy c-mean clustering which is used to clubbed together of the similarity intensity value into clusters. Also further eight feature extracted using Intensity based Histogram and GrayLevel Co-occurrence Matrix (GLCM). Now three classifiers learning algorithm applied in this system, first one is backpropogation neural network (BPNN) which consists of multi-layer perceptrons to solve the complex problem for the given inputs. Second one is convolution neural network (CNN) are the part of neural networks which have very effective in areas such as image recognition and image classification. Third is Support vector machine (SVM) which can be used for both classification and regression challenges. Each of one is evaluated performance based on different techniques. It found that SVM and CNN gives 88% accuracy for this work.