Artificial intelligence system for the automatic detection of Alzheimer’s disease using magnetic resonance imaging (MRI)

Abstract

Alzheimer’s disease (AD) is a neurodegenerative illness and is considered one of the main causes of dementia affecting millions of people. In its early stage – Mild-cognitive impairment (MCI) – is asymptomatic. Furthermore, although several studies have been made, until this day, no cure is yet available. Currently, some pharmaceuticals provide a slowing of symptoms if administered in early stages. However, to do so, the diagnosis needs to be properly performed to distinguish AD different stages. Thereby, there remains a growing need for early diagnosis to minimise AD impact by delaying it and its underlying effects. This work main purpose is to create an intelligent system that enables Alzheimer’s automatic detection using Magnetic Resonance Imaging (MRI). To do so, a set of MRI images were analysed in the sagittal, coronal, and axial anatomical views and certain features were extracted and pre-selected to feed machine learning classic algorithms and a deep learning algorithm. On the one hand, for the Machine Learning classic algorithms, and for the comparison between: (1) AD vs Control (CN), a Bagged Trees Classifier reached a discrimination accuracy of 93.3!; (2) AD vs MCI, Quadratic SVM classifier got a discrimination accuracy of 87.7!; (3) CN vs MCI, Fine KNN and Subspace KNN classifiers achieved a discrimination accuracy of 88.2!, respectively; and (4) All vs All, the Subspace KNN classifier provided a discrimination accuracy of 75.3!. On the other hand, for the Deep Learning algorithm, and for the comparison between: (1) AD vs CN, a discrimination accuracy of 82.2! was achieved; (2) AD vs MCI, got a discrimination accuracy of 75.4!; (3) CN vs MCI, reached a discrimination accuracy of 83.8!; and (4) All vs All, reached a discrimination accuracy of 64.0!. In the CN vs MCI comparison, the proposed method, when compared with methods that use structural MRI (sMRI), showed an increase in classification accuracy of 9%. Therefore, the potential of this work in the diagnosis of AD, mainly in its early stages, is reinforced.

Date of Award	16 Dec 2021
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Miguel Rodrigues (Supervisor)