detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2310.12574
Tema
Electrical Engineering and Systems... Computer Science - Computer Vision...Autor
Hoang, Gia Minh Lee, Youngjoo Kim, Jae GwanCategoría
Computer Science
Año
2023
fecha de cotización
10/7/2024
Palabras clave
model generalizability dataset disease classification alzheimerResumen
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
High-Frequency Repetitive Magnetic Stimulation at the Sacrum Alleviates Chronic Constipation in Parkinson’s Patients
magnetic stimulation parkinson’s significant patients scale sacrum pd hf-rms chronic constipation scores
