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