Brain Tumor Classification on Multi-Modality MRI Using Radiomic Features

Brain tumor corresponds to a group of diseases in which abnormal cells grow exponentially within the brain, the most important organ of the central nervous system. This disease is responsible for thousands of deaths worldwide. Early diagnosis and treatment increase the chance of survival. The most common technique to detect such tumors is analyzing MRI scans which carry subjectivity and lacks reproducibility. In order to improve the accuracy of such technique, a computer-aided system has been proposed, using Machine Learning and Image Processing to classify scans, characterizing such tumors, and acting as a second opinion. This paper benchmark several machine learning methods, such as ensemble and neural approaches, along with Radiomic features to classify MRI acquired on T1, T2, and FLAIR modalities, between healthy, glioma, meningioma, and pituitary tumor. Our experiments were conducted following K-Fold Cross-Validation and the best results were achieved by XGBoost and Deep Neural Network, in which XGBoost achieved an average accuracy of 88.51%and a sensitivity of 89.10 %, and the Deep Neural Network achieved 87.09% and a sensitivity of 87.38%. Such Computer Vision technique may assist radiologists to provide an accurate and efficient tumor diagnosis using MRI.