Multi-class Alzheimer’s disease (AD) classification using Swin Transformer wavelet and Gray Wolf Optimization (GWO)

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer’s disease (AD) is a slow-growing neurological disorder that destroys human thought and consciousness. This disease directly affects the development of mental ability and neurocognitive function. The number of Alzheimer’s patients is increasing day by day, especially in the elderly over 60 years of age, and it gradually becomes a cause of their death. Machine learning (ML) and deep learning (DL) approaches have been developed in the literature to improve the diagnosis and classification of AD. Machine learning approaches have cumbersome feature selection. Deep learning has been used in recent research because it automatically selects features. This research aims to present a Swin Transformer wavelet for Alzheimer’s classification based on structural MRI images in two-class, three-class and four-class modes. The proposed approach uses wavelet fusion in the Swin Transformer network to extract features. The outputs of the modified capsule are fed into a wavelet as feature vectors. The wavelet is a relevant feature selector in the proposed model. The Gray Wolf Optimization (GWO) method was used to find the model’s hyperparameters. The proposed approach achieved an accuracy of 0.9812 in 4-class classification, 0.9980 in 3-class classification, and 1.0 in 2-class classification. In the studies conducted in this research, the Swin Transformer wavelet+GWO model is the heaviest model in terms of the evaluation criteria Parameters(10e6), GFlops, and Memory (GB). This is while the EfficientNet model is the lightest in these criteria. • A hybrid deep learning framework combining Swin Transformer, wavelet transform, and Gray Wolf Optimization is proposed for multi-class Alzheimers disease MRI classification. • Wavelet decomposition enhances multi-scale frequency feature extraction, improving discrimination between dementia stages compared with baseline CNN models. The proposed model achieves high performance across 2-class, 3-class, and 4-class Alzheimers classification tasks, reaching up to 0.98120.9875 accuracy in four-class experiments. • Gray Wolf Optimization effectively tunes model hyperparameters and contributes to consistent performance improvements over transfer-learning baselines. • Grad-CAM visualization is incorporated to enhance interpretability and provide clinically meaningful insight into model decision regions.

Key findings

• This is while the EfficientNet model is the lightest in these criteria. • A hybrid deep learning framework combining Swin Transformer, wavelet transform, and Gray Wolf Optimization is proposed for multi-class Alzheimers disease MRI classification. • Wavelet decomposition enhances multi-scale frequency feature extraction, improving discrimination between dementia stages compared with baseline CNN models.

Why it matters

This study used a deep learning method combining a Swin Transformer, wavelet transform and Gray Wolf Optimization to classify MRI scans into two, three or four Alzheimer's categories, reporting high accuracy. It is a diagnostic classification tool only and does not test any treatment, cure or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1016/j.ibmed.2026.100362
• Original source →
• Free full text: Intelligence-Based Medicine →

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

@article{nejad2026-32aff6e9,
  author  = {Nejad, Aida Rezaei and Salimi, Faeze Sadat Sadati and Hemmasian, Mahdi and Mirzaee, Saeed and Abdiyeva, Khabiba and Mousa, Ramin and Hesaraki, Saba},
  title   = {{Multi-class Alzheimer’s disease (AD) classification using Swin Transformer wavelet and Gray Wolf Optimization (GWO)}},
  journal = {Intelligence-Based Medicine},
  year    = {2026},
  doi     = {10.1016/j.ibmed.2026.100362},
  url     = {https://doi.org/10.1016/j.ibmed.2026.100362}
}

Studies this one cites

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

• Mild cognitive impairment Lancet (London, England) · 2006-04-01 · usefulness score 64/100 · Prevention and risk
• A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks Journal of medical systems · 2019-12-18 · usefulness score 64/100 · Long-term cohort studies
• 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
• Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning NeuroImage · 2012-10-02 · usefulness score 51/100 · Mild cognitive impairment

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• Multi-scale 3D CNN with imbalance-aware training for structural MRI-based six-class Alzheimer disease staging Computers in Biology and Medicine · 2026-09-22 · usefulness score 48/100
• Progression-Aware and Explainable CNN–Transformer Framework for Multiclass Alzheimer’s Disease Staging Using MRI Diagnostics · 2026-02-16 · usefulness score 35/100
• Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease NeuroImage · 2011-10-04 · usefulness score 36/100
• MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI Research Square · 2026-10-23 · usefulness score 16/100

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More on Brain scans and imaging

• A review that covers this ground Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review Frontiers in aging neuroscience · 2017-10-06 · usefulness score 68/100
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