BCM-VEMT: classification of brain cancer from MRI images using deep learning and ensemble of machine learning techniques

Brain cancer is quite possibly the most common cause of death in recent years. Appropriate diagnosis of the cancer type empowers the specialists to make the right choice of treatment, decision, and to save the patient's life. It goes without saying the importance of a computer-aided diagnosis system with image processing that can classify the tumor types correctly. In this paper, an enhanced approach has been proposed that can classify brain tumor types from magnetic resonance images (MRI) using deep learning and an ensemble of machine learning (ML) algorithms. The system named BCM-VEMT can classify among four different classes that consist of three categories of brain cancers (Glioma, Meningioma, and Pituitary) and a non-cancerous class, which means normal type. A convolutional neural network was developed to extract deep features from the MRI images. These extracted deep features are fed into ML classifiers to classify among these cancer types. Finally, a weighted average ensemble of classifiers is used to achieve better performance by combining the results of each ML classifier. The dataset of the system has a total of 3787 MRI images of four classes. BCM-VEMT has achieved better performance with 97.90% accuracy for the Glioma class, 98.94% accuracy for Meningioma, 98.92% accuracy for Pituitary and 98.00% accuracy for the Normal class. BCM-VEMT can have great significance for medical sectors in classifying brain cancer types.