  Alzheimer's disease (AD) represents the primary form of neurodegeneration,
impacting millions of individuals each year and causing progressive cognitive
decline. Accurately diagnosing and classifying AD using neuroimaging data
presents ongoing challenges in medicine, necessitating advanced interventions
that will enhance treatment measures. In this research, we introduce a dual
attention enhanced deep learning (DL) framework for classifying AD from
neuroimaging data. Combined spatial and self-attention mechanisms play a vital
role in emphasizing focus on neurofibrillary tangles and amyloid plaques from
the MRI images, which are difficult to discern with regular imaging techniques.
Results demonstrate that our model yielded remarkable performance in comparison
to existing state of the art (SOTA) convolutional neural networks (CNNs), with
an accuracy of 99.1%. Moreover, it recorded remarkable metrics, with an
F1-Score of 99.31%, a precision of 99.24%, and a recall of 99.5%. These results
highlight the promise of cutting edge DL methods in medical diagnostics,
contributing to highly reliable and more efficient healthcare solutions.
