Hybrid Modeling Approach for the Forecasting of the Fibrilization Process and the Resulting AD

Abstract

Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro- deoxy-D-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD. These modalities and their particular utilities are discussed in this thesis. Machine learning and deep learning have demonstrated a wonderful performance in the classification task. Important progress has been made in image recognition, mainly due to the availability of large-scale annotated datasets and the revival of convolutional neural networks (CNNs). The aim of this study is to help the neurologists in selection of appropriate classification method based on several parameters like accuracy, computer complexity, and low training data availability. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies.