Multi-grade brain tumor classification using deep CNN with extensive data augmentation

Numerous computer-aided diagnosis (CAD) systems have been recently presented in the history of medical imaging to assist radiologists about their patients. For full assistance of radiologists and better analysis of magnetic resonance imaging (MRI), multi-grade classification of brain tumor is an essential procedure. In this paper, we propose a novel convolutional neural network (CNN) based multi-grade brain tumor classification system. Firstly, tumor regions from an MR image are segmented using a deep learning technique. Secondly, extensive data augmentation is employed to effectively train the proposed system, avoiding the lack of data problem when dealing with MRI for multi-grade brain tumor classification. Finally, a pre-trained CNN model is fine-tuned using augmented data for brain tumor grade classification. The proposed system is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.