Ensemble Transfer Learning for Brain Tumor Classification

Brain tumors are more commonly found in both children and the aged. It is a dangerous form of tumor resulting from the out-of-control growth of brain cells within the cranium. Tumor cells are not easily classified because of their heterogeneous nature. Convolutional neural networks (CNN) are deep learning approaches we use in general for image classification and recognition and in particular for medical images. In this paper, we use Transfer Learning process and employ multiple pre-trained CNN for extracting features from Magnetic Resonance (MR) images of the brain. The traditional Fully Connected (FC) layer classifier evaluates the extracted features. The two best-performing extracted features for the classifier are chosen and combined into an ensemble of features that are then introduced into the FC layer classifier to predict the final output. We used a dataset of 5712 MR images divided into 4 classes, normal and three kinds of brain tumors (Glioma, Meningioma, and Pituitary). The research results show that an ensemble of features can contribute to a significant improvement in performance.