The Classification of Brain Tumours by Means of Feature-Based Transfer Learning

Brain tumours are abnormal growths of cells in the brain and can be life-threatening if not detected early. Traditionally, radiologists manually assess magnetic resonance imaging (MRI) scans of the brain to identify and evaluate brain tumours; however, this process is prone to misinterpretation. This study investigates the application of a deep learning technique known as feature-based transfer learning to automate brain tumour detection from MRI images. A dataset of MRI scans labelled with different types of brain tumours was utilised in the study, in which a MobileNet pre-trained convolutional neural network was used to extract discriminative features from the images. The different classes of the tumors were then classified by three vanilla machine learning models, i.e., kNearest Neighbors (kNN), Support Vector Machine (SVM) and Logistic Regression (LR). The study showed that the MobileNet + LR pipeline could distinguish the classes well. The proposed method demonstrates its potential for augmenting and enhancing radiologist assessment of medical imaging.