Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer’s disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3–5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.

基于人工智能 (AI) 的方法对于各种医疗应用的计算机辅助诊断 (CAD) 至关重要。他们快速准确地从复杂数据中学习的能力非常出色。深度学习 (DL) 模型在准确分类阿尔茨海默病 (AD) 及其相关认知状态、早期轻度认知障碍 (EMCI) 和晚期轻度认知障碍 (LMCI) 以及被称为认知正常的健康状况方面显示出了可喜的结果。中）。这为疾病进展和诊断提供了宝贵的见解。然而，某些传统机器学习 (ML) 分类器的性能与 DL 模型同样好甚至更好，但需要的训练数据更少。这对于 CAD 中标记数据集有限的情况尤其有价值。在本文中，我们提出了一种基于 ML 模型的磁共振成像 (MRI) 数据集成分类器，其准确率达到了 96.52%。这比最佳个体分类器提高了 3-5%。我们使用阿尔茨海默病神经影像计划和开放获取系列影像研究数据集，在数据稀缺和数据丰富的条件下评估了流行的 AD 分类 ML 分类器。通过将结果与最先进的以 CNN 为中心的深度学习算法进行比较，我们可以深入了解每种方法的优点和缺点。这项工作将帮助用户根据数据可用性选择最合适的 AD 分类算法。