Benign and Malignant Tumor Classification from MRI Images using Modified Convolution Neural Network Approach

Abstract

Globally, according to the World Health Organization (W.H.O) report, a secondary major factor for the death of human beings is cancer. Due to the uncontrolled growth of abnormal tissues, it can start in any organ of the body and after some time spreads to the other parts of the body through the bloodstream. So, for effective treatment of a cancer patient, it is very necessary to diagnose the tumor/cancer cells in the early stage. In this proposed work a 12 layered Convolution Neural Network (CNN) model has been implemented for the classification of benign and malignant tumors in brain tumor MRI images. To avoid the shortage of data during model training, data augmentation and data shuffling have been used. A 10% and 20% dropout of neurons is used to avoid the overfitting of the model. This proposed model can predict the type of tumor (benign or malignant) is comparatively in less computational time with 99.22% of accuracy..