Document detail
ID
oai:arXiv.org:2310.12574
Topic
Electrical Engineering and Systems... Computer Science - Computer Vision...Author
Hoang, Gia Minh Lee, Youngjoo Kim, Jae GwanCategory
Computer Science
Year
2023
listing date
7/10/2024
Keywords
model generalizability dataset disease classification alzheimerAbstract
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.
Hoang, Gia Minh,Lee, Youngjoo,Kim, Jae Gwan, 2023, A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification
A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
ménière disease p < detection imaging sequences 3d-zoomit 3d endolymphatic real tse reconstruction ir inversion-recovery hydrops ratio
