Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI.

ABSTRACT:

Background

Alzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability.

Methods

We proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (

SUBMITTER: Chen X 

PROVIDER: S-EPMC9372697 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature