Deep learning model for prediction of progressive mild cognitive impairment to Alzheimer's disease using structural MRI
Document Type
Article
Publication Date
6-2-2022
Abstract
Alzheimer's disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer's disease permits the provision of preventive medication to slow the disease's progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer's disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.
Keywords
Alzheimer's disease, Deep learning, Prediction, Magnetic resonance imaging, Mild cognitive impairment
Divisions
biomedengine,mechanical
Funders
Universiti Malaya (Grant No: PV052-2019),ACU UK (Grant No: IF063-2021)
Publication Title
Frontiers in Aging Neuroscience
Volume
14
Publisher
Frontiers Media SA
Publisher Location
AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND