Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective medications or supplemental treatments. Thus, predicting AD progression is crucial for clinical practice and medical research. Due to limited neuroimaging data, two-dimensional convolutional neural networks (2D CNNs) have been commonly adopted to differentiate among cognitively normal subjects (CN), people with mild cognitive impairment (MCI), and AD patients. Therefore, this paper proposes an ensemble learning (EL) architecture based on 2D CNNs, using a multi-model and multi-slice ensemble. First, the top 11 coronal slices of grey matter density maps for AD versus CN classifications were selected. Second, the discriminator of a generative adversarial network, VGG16, and ResNet50 were trained with the selected slices, and the majority voting scheme was used to merge the multi-slice decisions of each model. Afterwards, those three classifiers were used to construct an ensemble model. Multi-slice ensemble learning was designed to obtain spatial features, while multi-model integration reduced the prediction error rate. Finally, transfer learning was used in domain adaptation to refine those CNNs, moving them from working solely with AD versus CN classifications to being applicable to other tasks. This ensemble approach achieved accuracy values of 90.36%, 77.19%, and 72.36% when classifying AD versus CN, AD versus MCI, and MCI versus CN, respectively. Compared with other state-of-the-art 2D studies, the proposed approach provides an effective, accurate, automatic diagnosis along the AD continuum. This technique may enhance AD diagnostics when the sample size is limited.

阿尔茨海默病 (AD) 是一种慢性神经退行性疾病，没有有效的药物治疗或补充治疗。因此，预测 AD 进展对于临床实践和医学研究至关重要。由于神经影像数据有限，二维卷积神经网络 (2D CNN) 已普遍用于区分认知正常受试者 (CN)、轻度认知障碍 (MCI) 和 AD 患者。因此，本文提出了一种基于 2D CNN 的集成学习 (EL) 架构，使用多模型和多切片集成。首先，选择了 AD 与 CN 分类的灰质密度图的前 11 个冠状切片。其次，生成对抗网络的判别器 VGG16 和 ResNet50 用选定的切片进行训练，并且使用多数投票方案来合并每个模型的多切片决策。之后，这三个分类器被用来构建一个集成模型。多切片集成学习旨在获取空间特征，而多模型集成降低了预测错误率。最后，在域适应中使用迁移学习来改进这些 CNN，将它们从仅用于 AD 与 CN 分类转移到适用于其他任务。在对 AD 与 CN、AD 与 MCI 以及 MCI 与 CN 进行分类时，这种集成方法分别实现了 90.36%、77.19% 和 72.36% 的准确度值。与其他最先进的 2D 研究相比，所提出的方法提供了沿 AD 连续体的有效、准确、自动诊断。