Document detail
ID
oai:arXiv.org:2402.11931
Topic
Computer Science - Sound Electrical Engineering and Systems... Quantitative Biology - Neurons and...Author
Zhang, Xiaohui Fu, Wenjie Liang, ManguiCategory
Computer Science
Year
2024
listing date
2/21/2024
Keywords
loss alzheimer diseaseAbstract
Alzheimer's disease is a common cognitive disorder in the elderly.
Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia.
At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants.
However, the recognition accuracy of current methods is unsatisfactory, and most of them focus on using low-dimensional handcrafted features to extract relevant information from audios.
This paper proposes an Alzheimer's disease detection system based on the pre-trained framework Wav2vec 2.0 (Wav2vec2).
In addition, by replacing the loss function with the Soft-Weighted CrossEntropy loss function, we achieved 85.45\% recognition accuracy on the same test dataset.
Zhang, Xiaohui,Fu, Wenjie,Liang, Mangui, 2024, Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection
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
