  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, brain tumor is one of the most common and aggressive malignant tumor
diseases which is leading to a very short expected life if it is diagnosed at
higher grade. Based on that, brain tumor grading is a very 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
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.
