Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning

2025-10-22

AI Summary

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.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Alzheimer disease is a severe brain disorder that causes harm in various brain areas and leads to memory damage. The limited availability of labeled medical data poses a significant challenge for accurate Alzheimer disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. To address this challenge, our study leverages the power of big data in the form of pre-trained Convolutional Neural Networks (CNNs) within the framework of Few-Shot Learning (FSL) and ensemble learning. We propose an ensemble approach based on a Prototypical Network (ProtoNet), a powerful method in FSL, integrating various pre-trained CNNs as encoders. This integration enhances the richness of features extracted from medical images. Our approach also includes a combination of class-aware loss and entropy loss to ensure a more precise classification of Alzheimer disease progression levels. The effectiveness of our method was evaluated using two datasets, the Kaggle Alzheimer dataset and the ADNI dataset, achieving an accuracy of 99.72% and 99.86%, respectively. The comparison of our results with relevant state-of-the-art studies demonstrated that our approach achieved superior accuracy and highlighted its validity and potential for real-world applications in early Alzheimer disease detection.

Problem

• Detecting Alzheimer disease with limited labeled medical data
• Improving detection accuracy using few-shot learning techniques
• Addressing data scarcity and privacy in Alzheimer diagnosis

Innovation

• Uses ensemble few-shot learning with pre-trained CNNs
• Integrates Prototypical Network with multiple encoder models
• Combines class-aware and entropy loss for precise classification