Towards Accurate Alzheimer’s Disease Diagnosis: Integrating Focused Linear Attention in Deep Learning Frameworks

The early stage and accurate diagnosis of Alzheimer’s Disease (AD) in neuroimaging remains a significant challenge. We introduce an innovative deep learning framework that incorporates a Focused Linear Attention (FLA) module to enhance the diagnosis of AD through medical imaging in this study. Our approach the strength of linear and softmax attention processes to enable the model to detect structural abnormalities and subtle patterns in brain pictures indicating various phases of cognitive decline. Our model achieves an accuracy of $93.21 \%$, together with a sensitivity of $92.74 \%$ and a precision of $94.01 \%$ in differentiating between non-demented, mild to moderately demented, and badly demented individuals according to experimental data. Our framework enhances overall diagnostic performance in multiclass classification by $1.85 \%$ when compared to traditional machine learning models and baseline deep learning architectures. The incorporation of FLA greatly enhances these improvements, transforming our model into a powerful resource for the early diagnosis and intervention in AD. The results highlight how advanced deep learning methods, particularly Focused Linear Attention, could transform the early detection of Alzheimer’s Disease.