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Updated: Mar 29, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Multi-Architecture Deep Learning for Early Alzheimer's Detection in MRI: Slice- and Scan-Level Analysis.

Isabelle Bricaud1, Giovanni Luca Masala1,

  • 1School of Computing, University of Kent, Canterbury CT2 7PE, UK.

International Journal of Environmental Research and Public Health
|March 28, 2026
PubMed
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This study highlights the critical role of preprocessing in deep learning for Alzheimer's disease (AD) detection. Optimized preprocessing significantly improves automated classification accuracy for AD and mild cognitive impairment (MCI) using MRI scans.

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with early detection crucial for intervention.
  • Structural MRI reveals brain changes in AD, but manual detection of subtle, early-stage alterations is challenging.
  • Automated methods are needed for reliable diagnosis, especially during prodromal stages like mild cognitive impairment (MCI).

Purpose of the Study:

  • To compare fifteen deep learning architectures for classifying AD, MCI, and cognitively normal (CN) subjects.
  • To investigate the impact of preprocessing pipelines on model reliability and performance.
  • To establish preprocessing quality as a key determinant for deep learning in neuroimaging.

Main Methods:

  • A dual-level evaluation framework (slice-level and scan-level) was used.
Keywords:
ADNI datasetAlzheimer’s diseaseconvolutional neural networksdeep learningearly Alzheimer’s detectionmedical imagingmulti-slice evaluationstructural MRItransfer learningvision transformers

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  • Fifteen deep learning models, including CNNs, Transformers, and hybrid architectures, were tested on the ADNI dataset.
  • Preprocessing steps were systematically optimized to reduce data variability and enhance feature consistency.

    • Main Results:

    • CNNs and hybrid pre-trained models outperformed Transformer-based models in both slice-level and scan-level classification.
    • ConvNeXtV2-L achieved the highest scan-level performance (91.07%), and EfficientNetV2-L the highest slice-level accuracy (86.84%).
    • Optimized preprocessing significantly influenced model performance, with some architectures showing substantial improvements and others degradation, indicating interdependence between architecture and aggregation strategy.

    Conclusions:

    • Robust and standardized preprocessing is essential for reliable deep learning in neuroimaging.
    • Carefully designed preprocessing enhances classification accuracy for AD detection.
    • This work provides a foundation for more reproducible and interpretable Alzheimer's disease detection pipelines.