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Deep Learning is the newest and the current trend of the machine learningfield that paid a lot of the researchers' attention in the recent few years. Asa proven powerful machine learning tool, deep learning was widely used inseveral applications for solving various complex problems that requireextremely high accuracy and sensitivity, particularly in the medical field. Ingeneral, brain tumor is one of the most common and aggressive malignant tumordiseases which is leading to a very short expected life if it is diagnosed athigher grade. Based on that, brain tumor grading is a very critical step afterdetecting the tumor in order to achieve an effective treating plan. In thispaper, we used Convolutional Neural Network (CNN) which is one of the mostwidely used deep learning architectures for classifying a dataset of 3064 T1weighted contrast-enhanced brain MR images for grading (classifying) the braintumors into three classes (Glioma, Meningioma, and Pituitary Tumor). Theproposed CNN classifier is a powerful tool and its overall performance withaccuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while theresults for the uncropped lesions are 99% accuracy and 98.52% sensitivity andthe results for segmented lesion images are 97.62% for accuracy and 97.40%sensitivity.

Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes