Multi-Architecture Deep Learning for Early Alzheimer’s Detection in MRI: Slice- and Scan-Level Analysis

TL;DR

This paper introduces a deep learning framework for early Alzheimer’s detection using MRI scans, emphasizing the importance of standardized preprocessing to improve diagnostic reliability.

Contribution

The study introduces a dual-level evaluation framework comparing multiple deep learning architectures and highlights the critical role of standardized preprocessing in neuroimaging.

Findings

CNNs and hybrid pre-trained models outperformed Transformer-based models in classifying Alzheimer’s disease, MCI, and cognitively normal subjects.

Standardized preprocessing significantly improved model reliability and classification accuracy in both slice-level and scan-level analyses.

Abstract

Public health relevance—How does this work relate to a public health issue?
Alzheimer’s disease is a major global public health challenge, causing substantial mortality, disability, and economic and caregiving burden in ageing populations.Alzheimer’s disease is the most common cause of dementia and is associated with progressive memory and cognitive decline.

Alzheimer’s disease is a major global public health challenge, causing substantial mortality, disability, and economic and caregiving burden in ageing populations.

Alzheimer’s disease is the most common cause of dementia and is associated with progressive memory and cognitive decline.

Public health significance—Why is this work of significance to public health?
Earlier and more reliable detection may slow disease progression, preserve independence, and reduce long-term healthcare and societal costs.Emphasising standardised MRI image preprocessing improves the reliability and reproducibility of automated diagnostic tools for population-level use.

Earlier and more reliable detection may slow disease progression, preserve independence, and reduce long-term healthcare and societal costs.

Emphasising standardised MRI image preprocessing improves the reliability and reproducibility of automated diagnostic tools for population-level use.

Public health implications—What are the key implications or messages for practitioners, policy makers and/or researchers in public health?
AI-assisted MRI analysis may assist clinicians in identifying high-risk individuals earlier, particularly where specialist resources are limited.Investment in standardised neuroimaging pipelines can support scalable, equitable early detection strategies for Alzheimer’s disease.

AI-assisted MRI analysis may assist clinicians in identifying high-risk individuals earlier, particularly where specialist resources are limited.

Investment in standardised neuroimaging pipelines can support scalable, equitable early detection strategies for Alzheimer’s disease.

Alzheimer’s disease (AD), the most common form of dementia, is a progressive and irreversible neurodegenerative disorder. Structural MRI is widely used for diagnosis, revealing brain changes associated with AD. However, these alterations are often subtle and difficult to detect manually, particularly at early stages. Early intervention during prodromal stages, such as mild cognitive impairment (MCI), can help slow disease progression, highlighting the need for reliable automated methods. In this work, we introduce a dual-level evaluation framework comparing fifteen deep learning architectures, including convolutional neural networks (CNNs), Transformers, and hybrid models, for classifying AD, MCI, and cognitively normal (CN) subjects using the ADNI dataset. A central focus of our work is the impact of robust and standardized preprocessing pipelines, which we identified as a critical yet underexplored factor influencing model reliability. By evaluating performance at both slice-level and scan-level, we reveal that multi-slice aggregation affects architectures asymmetrically. By systematically optimizing preprocessing steps to reduce data variability and enhance feature consistency, we established preprocessing quality as an essential determinant of deep learning performance in neuroimaging. Experimental results show that CNNs and hybrid pre-trained models outperform Transformer-based models in both slice-level and scan-level classification. ConvNeXtV2-L achieved the best scan-level performance (91.07%), EfficientNetV2-L the highest slice-level accuracy (86.84%), and VGG19 balanced results (86.07%/88.52%). ConvNeXtV2-L and SwinV1-L exhibited scan-level improvements of 7.60% and 9.04% respectively, while EfficientNetV2-L experienced degradation of 2.66%, demonstrating that architectural selection and aggregation strategy are interdependent factors. These findings suggest that carefully designed preprocessing not only improves classification accuracy but may also serve as a foundation for more reproducible and interpretable Alzheimer’s disease detection pipelines.

Figures