A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.

ABSTRACT: Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest

SUBMITTER: Ahmadi M 

PROVIDER: S-EPMC10965088 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature