Advancing personalized neurology with explainable AI in Alzheimer’s classification using NeuroX-DualFusion framework

Alzheimer’s disease classification from MRI slices is a cornerstone of personalized neurology, enabling patient-specific diagnosis and treatment planning. Traditional machine learning approaches often fail in this domain due to class imbalance, limited feature representation, and poor interpretability, which restrict their clinical adoption and typically leading to biased predictions and unstable subject-level outcomes. This research introduces NeuroX-DualFusion, a hybrid framework that integrates a local attention stream and a global convolutional stream to capture both fine-grained and contextual features. The pipeline begins with standardized preprocessing and data augmentation to enhance anatomical clarity and mitigate class imbalance. Segmentation via attention-based U-Net isolates critical brain regions, while proposed NeuroX-DualFusion, dual-stream feature extraction enables robust representation learning. Additionally, Grad-CAM visualizations provide transparent, class-specific interpretability, highlighting discriminative regions aligned with clinical markers. Quantitative evaluation across Accuracy (97.5%), Precision (96.5%), Recall (97.5%), F1-score (96.5%), and Specificity (98.5%), demonstrates that NeuroX-DualFusion outperforms individual models, achieving subject-level accuracy. These findings underscore the potential of NeuroX-DualFusion to advance personalized neurology by delivering reliable, interpretable, and patient-centered dementia stage classification using MRI data, bridging the gap between computational innovation and clinical practice.