  Alzheimer's disease (AD), characterized by progressive cognitive decline and
memory loss, presents a formidable global health challenge, underscoring the
critical importance of early and precise diagnosis for timely interventions and
enhanced patient outcomes. While MRI scans provide valuable insights into brain
structures, traditional analysis methods often struggle to discern intricate 3D
patterns crucial for AD identification. Addressing this challenge, we introduce
an alternative end-to-end deep learning model, the 3D Hybrid Compact
Convolutional Transformers 3D (HCCT). By synergistically combining
convolutional neural networks (CNNs) and vision transformers (ViTs), the 3D
HCCT adeptly captures both local features and long-range relationships within
3D MRI scans. Extensive evaluations on prominent AD benchmark dataset, ADNI,
demonstrate the 3D HCCT's superior performance, surpassing state of the art CNN
and transformer-based methods in classification accuracy. Its robust
generalization capability and interpretability marks a significant stride in AD
classification from 3D MRI scans, promising more accurate and reliable
diagnoses for improved patient care and superior clinical outcomes.
