DAMNet: Dynamic mobile architectures for Alzheimer's disease

Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3 % in validation and 99.9 % in testing phases. Despite a 20 % pruning rate, DAMNet maintains consistent performance with less than 0.2 % loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7 % F1 score within 805 s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data. •DAMNet balances accuracy and efficiency for AD models, reducing size by 20% with pruning and just 0.2% performance loss.•DAMNet uses global attention, multi-scale features, and ARP, converting 3D MRI to 2D for AD with a 95.7% F1 score.•Proposed a parallel intelligent framework for early AD, optimizing imaging and accelerating pathological feature detection.•The framework enhances diagnostic speed and accuracy via self-learning and optimization, ensuring rapid clinical feedback.