Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification

Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification

Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global...

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Bibliographic Details
Main Authors: Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Brain Sciences
Subjects:
Alzheimer’s disease
deep learning
convolutional neural networks
vision transformers
adaptive feature fusion
MRI classification
Online Access:https://www.mdpi.com/2076-3425/15/6/612
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