Alzheimer's disease is one of the most common types of neurodegenerative disease, characterized by the accumulation of amyloid-beta plaque and tau tangles. Recently, deep learning approaches have shown promise in Alzheimer's disease diagnosis. In this study, we propose a reproducible model that utilizes a 3D convolutional neural network with a dual attention module for Alzheimer's disease classification. We trained the model in the ADNI database and verified the generalizability of our method in two independent datasets (AIBL and OASIS1). Our method achieved state-of-the-art classification performance, with an accuracy of 91.94% for MCI progression classification and 96.30% for Alzheimer's disease classification on the ADNI dataset. Furthermore, the model demonstrated good generalizability, achieving an accuracy of 86.37% on the AIBL dataset and 83.42% on the OASIS1 dataset. These results indicate that our proposed approach has competitive performance and generalizability when compared to recent studies in the field.

翻译：阿尔茨海默病是最常见的神经退行性疾病类型之一，其特征是β-淀粉样蛋白斑块和tau蛋白缠结的积累。近年来，深度学习方法在阿尔茨海默病诊断中显示出潜力。在本研究中，我们提出了一种可复现的模型，该模型利用具有双重注意力模块的三维卷积神经网络进行阿尔茨海默病分类。我们在ADNI数据库中训练了该模型，并在两个独立数据集（AIBL和OASIS1）上验证了我们方法的泛化能力。我们的方法实现了最先进的分类性能，在ADNI数据集上，MCI进展分类的准确率达到91.94%，阿尔茨海默病分类的准确率达到96.30%。此外，该模型表现出良好的泛化能力，在AIBL数据集上的准确率达到86.37%，在OASIS1数据集上的准确率达到83.42%。这些结果表明，与近期该领域的研究相比，我们提出的方法具有竞争性的性能和泛化能力。