Automatic Classification of Brain Tumor and Alzheimer's Disease in MRI

Abstract

Full Name : Bashayer Fouad Marghalani Thesis Title : Automatic Classification of Brain Tumor and Alzheimer's Disease in MRI Major Field : Computer Vision Date of Degree: 24 December 2019

Computer vision (CV) and image processing techniques aim at the fast development of medical images diagnoses field. As the specialist takes a long time to diagnose one MRI images, CV techniques and machine learning algorithms make the process faster than the manual method. Consequently, these techniques save time and effort. In this thesis, an intelligent method has been used for the detection and classification of brain pathologies like tumors, Alzheimer's disease (AD), and healthy brain images. The algorithm used encompasses 4 stages: Magnetic Resonance Imaging (MRI) image acquisition, pre-processing, feature extraction, and classification. In this thesis, the Bag of Features module has been used for the classification of the MRI of brain with tumor, MRI of brain of Alzheimer's disease patients, and MRI of normal brain. In this thesis, the average classification accuracy achieved for all three classes is 98%. Furthermore, this thesis has got 98% sensitivity and 99% specificity.

Keywords: Alzheimer's, tumor, brain, Bag of Features, brain MRI, tumor segmentation, machine learning, computer vision, deep learning, Support Vector Machine, Convolutional Neural Networks, Speeded Up Robust Features, median filter.