Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification

Topic: Amyloid, tau and blood-test diagnosis

The proposed Deep-CNN and attention model focuses the model capacity on diagnostically relevant regions. The proposed model achieved an accuracy of 97%, which is higher than existing methods like SVM with kernels (90.5%), SVM Gaussian radial basis kernel (85%), and traditional CNN (93.5%). The visualizations of attention mechanism are used to increase the interpretability and demonstrate the attention maps which are align with known AD biomarkers. These results indicates that the attention-guided deep models can both improve multi-class MRI classification accuracy and provide clinically useful regional explanations.

Key findings

• The proposed Deep-CNN and attention model focuses the model capacity on diagnostically relevant regions.
• The proposed model achieved an accuracy of 97%, which is higher than existing methods like SVM with kernels (90.5%), SVM Gaussian radial basis kernel (85%), and traditional CNN (93.5%).
• The visualizations of attention mechanism are used to increase the interpretability and demonstrate the attention maps which are align with known AD biomarkers.
• These results indicates that the attention-guided deep models can both improve multi-class MRI classification accuracy and provide clinically useful regional explanations.

Why it matters

This study describes a deep convolutional neural network with an attention mechanism that classified Alzheimer's disease from MRI images with 97% accuracy, higher than several comparison methods. The attention maps aligned with known Alzheimer's disease biomarkers, which the authors say provides clinically useful regional explanations.

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Study details

• Effect size90.5%; 93.5%
• Evidence strengthLow

Read the original

• DOI: 10.3389/fradi.2025.1698760
• PubMed: 41613767
• Original source →
• Free full text: Frontiers in Radiology →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{lakshmanan2026-41613767,
  author  = {Lakshmanan, Sathish Kumar and Muthusamy, Maragatharajan and Dhanaraj, Rajesh Kumar and Aanjankumar, S. and Sayeed, Md Shohel and Mohamed, Mohamed Yasin Noor and Rathinam, Gopal},
  title   = {{Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification}},
  journal = {Frontiers in Radiology},
  year    = {2026},
  doi     = {10.3389/fradi.2025.1698760},
  pmid    = {41613767},
  url     = {https://doi.org/10.3389/fradi.2025.1698760}
}

Studies this one cites

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

• A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study The Lancet. Digital health · 2022-09-30 · usefulness score 67/100 · Caregiving
• A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features Frontiers in neuroinformatics · 2020-06-11 · usefulness score 56/100 · Brain scans and imaging
• 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
• Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population NeuroImage · 2010-12-10 · usefulness score 51/100 · Mild cognitive impairment
• Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach Frontiers in neuroscience · 2015-09-01 · usefulness score 50/100 · Mild cognitive impairment
• Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases Human brain mapping · 2020-07-02 · usefulness score 47/100 · Digital health tools
• Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI Computers in Biology and Medicine · 2025-03-30 · usefulness score 46/100 · Brain scans and imaging

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• Convolutional Neural Network Achieves 97.67 Percent Accuracy for Alzheimer MRI Classification Academia Open · 2026-02-17 · usefulness score 32/100
• Application of Convolutional Neural Network Based on ResNet18 for Alzheimer Disease Classification International Journal of Artificial Intelligence Research · 2025-12-30 · usefulness score 28/100
• A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease Brain sciences · 2020-02-05 · usefulness score 49/100
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