Enhanced Deep Learning Framework for Precise MRI ‐Based Alzheimer's Disease Stage Classification

Topic: Amyloid, tau and blood-test diagnosis

ABSTRACT Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in‐depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in‐depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine‐tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher‐order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high‐accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.

Key findings

• This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.

Why it matters

This study used a deep learning model to classify stages of Alzheimer's disease from MRI scans, achieving 94.2% accuracy. It is a detection and staging method only and does not test any treatment, cure, or prevention.

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Study details

• Effect size94.2%
• Evidence strengthLow

Read the original

• DOI: 10.1111/coin.70123
• Original source →

May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it.

@article{chandrasekaran2025-8eadc3a2,
  author  = {Chandrasekaran, Saravanan and Bhatia, Surbhi and Gupta, Muskan and Mahesh, T R and Alqhatani, Abdulmajeed and Almusharraf, Ahlam I.},
  title   = {{Enhanced Deep Learning Framework for Precise MRI ‐Based Alzheimer's Disease Stage Classification}},
  journal = {Computational Intelligence},
  year    = {2025},
  doi     = {10.1111/coin.70123},
  url     = {https://doi.org/10.1111/coin.70123}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs Scientific reports · 2022-10-17 · usefulness score 54/100 · Long-term cohort studies
• Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI Frontiers in bioengineering and biotechnology · 2020-10-30 · usefulness score 51/100 · Digital health tools
• ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis Telematics and Informatics Reports · 2025-02-14 · usefulness score 48/100 · Brain scans and imaging
• Stacked CNN-based multichannel attention networks for Alzheimer disease detection Scientific Reports · 2025-02-17 · usefulness score 48/100 · Brain scans and imaging

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