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

Publication Title

Frontiers in Aging Neuroscience

Divisions

biomedengine,mechanical

Funders

Universiti Malaya (Grant No: PV052-2019),ACU UK (Grant No: IF063-2021)

Volume

14

Publisher

Frontiers Media SA

Publisher Location

AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND

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