Continuous Learning for Automated Early‐Stage Alzheimer’s Detection Using MRI and Spike Neural Signals

Alzheimer’s disease (AD), a progressive neurodegenerative disorder whose symptoms become apparent late in the disease process but are only in the early stages of development, creates challenges that demand that this disorder be diagnosed early to reduce its progression. This research work has also suggested a lifelong learning system that insists on the combination of MRI and spike neural signals (EEG data) for the early detection of AD using automated deep learning. By recurrent catastrophic forgetting through elastic weight consolidation (EWC) and memory replay, the model learns from new data while retaining past knowledge that is vital in health‐care‐related environments where patient information is ever‐expanding. Three levels of integration between MRI and spike neural data have been used in this work: early, mid‐, and late fusion. Experimental results show that mid‐fusion gives better performance than other approaches of 86% accuracy, 84% sensitivity, and 88% specificity for AD identification, which can provide the structure of MRI and temporal EEG signals to identify the AD patient. Early fusion proved a capability to integrate general MRI‐EEG correspondences effectively, as integrated late fusion showed the capacity for handling diverse qualities of inputs by analyzing both models independently. The results of the study affirm the effectiveness of the continuous learning multimodal fusion strategy in improving both sensitivity to early AD biomarkers and data heterogeneity. The proposed approach seems to be promising for scalable and real‐time diagnostic solutions suitable to bring heterogeneous clinical data and the incremental nature of medical data into the diagnosis of early‐stage AD and patient management.