CNN Framework for Tumor Classification in MR Brian Images

Abstract

Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the
researchers' attention in the recent few years. As a proven powerful machine learning tool, deep
learning was widely used in several applications for solving various complex problems that require
extremely high accuracy and sensitivity, particularly in the medical field. In general, the brain tumor
is one of the most common and aggressive malignant tumor diseases which is leading to a noticeably
short expected life if it is diagnosed at a higher grade. Based on that, brain tumor classification is an
overly critical step after detecting the tumor in order to achieve an effective treating plan. In this
paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep
learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR
images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and
Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with an
accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the
uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion
images are 97.62% for accuracy and 97.40% sensitivity.