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

arXiv: https://arxiv.org/abs/2510.19282 · HTML: https://mlsanity.com/paper/2510.19282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021)
- [Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images](https://mlsanity.com/paper/2006.13276/) (2020)
- [Few-Shot Object Detection: A Comprehensive Survey](https://mlsanity.com/paper/2112.11699/) (2021)
- [Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images](https://mlsanity.com/paper/2105.08199/) (2021)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification](https://mlsanity.com/paper/2101.03526/) (2021)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [Few-shot Learning for CT Scan based COVID-19 Diagnosis](https://mlsanity.com/paper/2102.00596/) (2021)
- [COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images](https://mlsanity.com/paper/2301.01679/) (2023)

## Abstract (arXiv)

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.