DL-CNN Framework for Medical Image Analysis

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.