Classification of Structural MRI for Detecting Alzheimer’s Disease

Year 2016, Volume: 4 Issue: Special Issue-1, 195 - 198, 26.12.2016

Ayşe Demirhan

Abstract

Alzheimer’s Disease (AD) is a pathological form of dementia that degenerates
brain structures. AD affects millions of elderly people over the world and the
number of people with AD doubles every year. Detecting AD years before the
effects of disease using structural magnetic resonance imaging (MRI) of the
brain is possible. Neuroimaging features that are extracted from the structural
brain MRI can be used to predict AD by revealing disease related patterns.
Machine learning techniques can detect AD and predict conversions from mild
cognitive impairment (MCI) to AD automatically and successfully by using these
neuroimaging features. In this study common structural brain measures such as
volumes and thickness of anatomical structures that are obtained from The Open
Access Series of Imaging Studies (OASIS) and made publicly available by
https://www.nmr.mgh.harvard.edu/lab/mripredict are analysed. State-of-the-art
machine learning techniques, namely support vector machines (SVM), k-nearest
neighbour (kNN) algorithm and backpropagation neural network (BP-NN) are
employed to discriminate AD and mild AD from healthy controls. Training
hyperparameters of the classifiers are tuned using classification accuracy
which is obtained with 5-fold cross validation. Prediction performance of the
techniques are compared using accuracy, sensitivity and specificity. Results of
the system revealed that AD can be distinguished from the healthy controls
successfully using multivariate morphological features and machine learning
tools. According to the performed experiments SVM is the most successful
classifier for detecting AD with classification accuracies up to 82%.

Keywords

Alzheimer’s Disease, neuroimaging, structural MRI, multivariate analysis, image classification, machine learning techniques

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