Hybridized Deep Learning Approach for Detecting Alzheimer's Disease.

ABSTRACT: Alzheimer's disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease's early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the e

SUBMITTER: Balaji P 

PROVIDER: S-EPMC9855764 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature