  Alzheimer diseases (ADs) involves cognitive decline and abnormal brain
protein accumulation, necessitating timely diagnosis for effective treatment.
Therefore, CAD systems leveraging deep learning advancements have demonstrated
success in AD detection but pose computational intricacies and the dataset
minor contrast, structural, and texture variations. In this regard, a novel
hybrid FME-Residual-HSCMT technique is introduced, comprised of residual CNN
and Transformer concepts to capture global and local fine-grained AD analysis
in MRI. This approach integrates three distinct elements: a novel CNN Meet
Transformer (HSCMT), customized residual learning CNN, and a new Feature Map
Enhancement (FME) strategy to learn diverse morphological, contrast, and
texture variations of ADs. The proposed HSCMT at the initial stage utilizes
stem convolution blocks that are integrated with CMT blocks followed by
systematic homogenous and structural (HS) operations. The customized CMT block
encapsulates each element with global contextual interactions through
multi-head attention and facilitates computational efficiency through
lightweight. Moreover, inverse residual and stem CNN in customized CMT enables
effective extraction of local texture information and handling vanishing
gradients. Furthermore, in the FME strategy, residual CNN blocks utilize
TL-based generated auxiliary and are combined with the proposed HSCMT channels
at the target level to achieve diverse enriched feature space. Finally, diverse
enhanced channels are fed into a novel spatial attention mechanism for optimal
pixel selection to reduce redundancy and discriminate minor contrast and
texture inter-class variation. The proposed achieves an F1-score (98.55%), an
accuracy of 98.42% and a sensitivity of 98.50%, a precision of 98.60% on the
standard Kaggle dataset, and demonstrates outperformance existing ViTs and CNNs
methods.
