Abstract
Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning (DL) models helped the health industry in getting accurate diseases diagnosis. This article concentrates on the classification of magnetic resonance (MR) images. The objective is to differentiate between glioma tumors, meningioma tumors, pituitary tumors, and normal cases. Four deep convolutional neural networks are considered and compared in this article. These networks are inceptionresnetv2, inceptionv3, transfer learning, and BRAIN-TUMOR-net. A transfer-learning strategy is considered to enhance the performance of pre-trained models and save the time of training. The used dataset is the brain tumor magnetic resonance imaging dataset. It contains four classes including 826 MR images for glioma tumor, 822 MR images for meningioma tumor, 827 MR images for pituitary tumor, and 835 MR images for normal cases. Due to the limited number of images, we use the augmentation strategy to enlarge the size of the dataset. 75% of the data are considered for training and the other 25% are considered for testing. Segmentation of the classified results is performed. Simulation results prove that the DL model from scratch obtains the highest performance with the augmented data. In addition, a new practical implementation is presented for the proposed models.