Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning
Addressing critical challenges in early Alzheimer’s disease (AD) detection—including severe scarcity of labeled data, complex neuropathology, and stringent medical data privacy constraints—this paper proposes a prototype-based few-shot ensemble deep learning framework. Methodologically, it integrates multi-source pretrained CNNs to extract multi-scale features from medical neuroimaging; further, it introduces a novel joint optimization mechanism combining class-aware loss and entropy regularization to enhance discriminability and generalizability under few-shot conditions. Evaluated on the Kaggle Alzheimer and ADNI public benchmarks, the model achieves classification accuracies of 99.72% and 99.86%, respectively—substantially outperforming state-of-the-art methods. This work establishes a scalable, privacy-preserving technical paradigm for accurate, low-label-cost early AD screening.