Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.

ABSTRACT: Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction o

SUBMITTER: Agarwal D 

PROVIDER: S-EPMC8587338 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature