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Synapse
October 2, 20250 citations

Hybrid Deep Learning for Alzheimer's Disease Classification Using Multi-Layer U-Net

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

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Authors

MZMuhammad ZubairAJArfan JaffarSHSadaf Hussain

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Overview

This analysis shows improved classification accuracy of Alzheimer's stages in diverse patients, indicating potential for AI-assisted diagnostics.

Key Points

  • The proposed method achieves an overall accuracy of 97.78% for classifying Alzheimer's disease stages.
  • Results include precision rates of 97.18% for Alzheimer's, 97.78% for Cognitively Normal, and 97.03% for Mild Cognitive Impairment.
  • Using multi-layer U-Net for gray matter segmentation and multi-scale EfficientNet for feature extraction demonstrates effectiveness.
  • Improving explainability with XAI techniques enhances model reliability for clinical decisions.

Cite This Study

Zubair et al. (2025) studied this question.

synapsesocial.com/papers/68de5d9383cbc991d0a1ffa5https://doi.org/10.1371/journal.pone.0332572
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Intelligent Data Processing for Alzheimer's Disease Using Deep Learning2024
  2. 2Classifying alzheimer's disease from sMRI data using a hybrid deep learning approaches2024 · 4 citations
  3. 3Intelligent Alzheimer's Disease Diagnosing Using a Deep Learning Model2025
  4. 4Alzheimer’s Multiclassification Using Explainable AI Techniques2024 · 15 citations
  5. 5NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis2025 · 13 citations