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October 23, 2025Al-Iraqia Journal of Scientific Engineering ResearchOpen Access

Multimodal Deep Learning (DL) for Early Alzheimer's Disease (AD) Detection: Leveraging MRI and Clinical Data

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Authors

IHIdress Mohammed HusienUniversity of KirkukMAMohammed AhmedUniversity of KirkukMÖMete ÖzbaltanIzmir University

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Implication

Multimodal deep learning improves diagnostic performance in Alzheimer’s disease, suggesting enhanced early detection capabilities.

Key Points

  • Accuracy reached 97% using a multimodal approach to analyze MRI and clinical data, showcasing its potential.
  • The study employed an ensemble learning strategy, incorporating predictions from multiple modalities for robust performance.
  • Analysis utilized a multi-layer perceptron and deep learning techniques to improve Alzheimer's diagnosis effectiveness.
  • The framework's insights into model decisions through techniques like Grad-CAM are vital for clinical adoption.

Cite This Study

Husien et al. (2025) studied this question.

synapsesocial.com/papers/68f9d6583f378872224924e9https://doi.org/10.58564/ijser.4.3.2025.322
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Also Consider

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  4. 4Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review2018 · 308 citations
  5. 5Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosis2022 · 165 citations