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

Abstract--Alzheimer's 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's disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer's disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. T o 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's 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's disease detection. Index T erms--Few-shot learning, prototypical network, ensemble learning, transfer learning, pre-trained models, healthcare, Alzheimer disease. LZHEIMER'S disease is a progressive neurodegenera-tive disorder that mainly affects the elderly and causes memory loss and severe cognitive decline. The advances in medical imaging technologies, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), have opened new avenues for the analysis and understanding of this severe disease [1], [2]. Employing data analytics on these images helps to provide detailed insights about the structural and functional changes in the brain caused by this disease, which facilitates the early diagnosis and monitoring of disease progression [3]. However, the application of traditional Machine Learning (ML) techniques in analyzing medical images for Alzheimer's disease diagnosis faces significant challenges [4]. One of the primary limitations is the scarcity of labeled data.