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The growth of abnormal cells in the brain causes brain tumours. Statistical calculations on the population of the United States reveal that amongst those diagnosed with a brain tumour, the percentage of patients who have for five years or more after the tumour is discovered is only approximately 36 diagnosis of a brain tumour leads to a decreased chance of a patient's demise. The classic procedure used in general hospitals to diagnose brain tumours is magnetic resonance imaging. Once the MRI reveals the presence of abnormal cell growth, a biopsy is performed on the tissue sample of the suspected tumour mass. Examination of biopsy results helps classify the type of tumour. The aim of this project is split into two sections. The first one is to use MRI scans of the brain to identify the exact type of tumour present in the brain using a Convolutional Neural Network with a static Graphic User Interface to make the results easily accessible. The second section of this project is to compare different types of convolutional neural networks (Modified CNN, VGG16 and ALEXNet) on various parameters to determine which of those would be the most helpful with our project necessities. [ABSTRACT FROM AUTHOR]