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Synapse
February 5, 2026

Integrated Residual with Combined Temporal Module U-Net for Alzheimer's Disease Progression Prediction

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Authors

KLK R LathakumariHLHemalatha K. LPCPuttamadappa C

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Overview

Demonstrates improved Alzheimer’s Disease progression prediction using a deep learning model, suggesting better diagnosis tools are possible.

Key Points

  • To enhance prediction of Alzheimer's Disease progression using a new deep learning model called IRCTMU-Net.
  • Developed the Integrated Residual with Combined Temporal Module U-Net (IRCTMU-Net) for analysis.
  • Preprocessed images and utilized a Residual U-Net structure consisting of encoder and decoder layers.
  • Incorporated an attention module to capture local and global relationships in the data.
  • Conducted experiments on the ADNI dataset and compared results with existing models.
  • Achieved an accuracy of 99.80% and precision of 99.83% with IRCTMU-Net.
  • Outperformed existing models like Temporal Graph Attention (TGN) in predictive performance.

Cite This Study

Lathakumari et al. (2025) studied this question.

synapsesocial.com/papers/698433c8f1d9ada3c1fb12f4https://doi.org/10.1051/itmconf/20257901058/pdf
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Also Consider

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

  1. 1Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection2024 · 15 citations
  2. 2NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis2025 · 13 citations
  3. 3Revolutionizing Alzheimer's Disease Prediction Using EfficientNetB62024 · 4 citations
  4. 4Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.2025
  5. 5A novel interpreted deep network for Alzheimer’s disease prediction based on inverted self attention and vision transformer2025 · 3 citations