Self-Attention Augmented Wasserstein Generative Adversarial Network-based Detection of Brain Alzheimer Disease Using MRI

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer’s disease (AD) is a progressive neurological condition that leads to dementia. This study presents the SelfAttention Augmented Wasserstein Generative Adversarial Network (SAA-WGAN) for classifying AD stages, utilizing images from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Input images were pre-processed with Fast Guided Median Filter (FGMF) to enhance quality and reduce noise. Data augmentation techniques, including rotations and cropping, addressed class imbalances and improved training diversity. The SAA-WGAN model was validated across varying batch sizes, employing both augmented and non-augmented datasets to assess generalization capabilities. The technique was applied to 1,296 images, achieving a peak accuracy of 99% and demonstrating improved performance over conventional methods in key metrics such as AUC, Precision, and Sensitivity. These results highlight the model's effectiveness and potential to enhance diagnostic accuracy for Alzheimer's disease stages.

Key findings

• These results highlight the model's effectiveness and potential to enhance diagnostic accuracy for Alzheimer's disease stages.

Why it matters

This study describes a deep learning model that classified Alzheimer's disease stages from MRI images with high accuracy. These results are about diagnostic classification and do not address treating, curing, or preventing the disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.47857/irjms.2025.v06i01.02645
• Original source →
• Free full text: International Research Journal of Multidisciplinary Scope →

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

@article{zakariya2025-515ff4ed,
  author  = {Zakariya, S. M. and Umar, M. Sarosh},
  title   = {{Self-Attention Augmented Wasserstein Generative Adversarial Network-based Detection of Brain Alzheimer Disease Using MRI}},
  journal = {International Research Journal of Multidisciplinary Scope},
  year    = {2025},
  doi     = {10.47857/irjms.2025.v06i01.02645},
  url     = {https://doi.org/10.47857/irjms.2025.v06i01.02645}
}

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