Enhancement of Brain Tumor Detection on MRI Imaging Using AlexNet

Abstract

Brain tumors are the second leading cause of cancer death in children under age 15 and the second fastest growing cause of cancer death among those over age 65. Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolution neural network is proposed to classify different brain tumor types using two publicly available datasets. Alex Net is a deeper architecture with 8 layers which means that is better able to extract features. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV).