DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). However, the hypoattenuation in NCCT is difficult to precisely identify, and accurate assessments of the Alberta Stroke Program Early CT Score (ASPECTS...

Full description

Bibliographic Details
Main Authors: Shih-Yen Lin, Pi-Ling Chiang, Meng-Hsiang Chen, Meng-Yang Lee, Wei-Che Lin, Yong-Sheng Chen
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:NeuroImage: Clinical
Subjects:
Acute ischemic stroke
Alberta Stroke Program Early CT Score (ASPECTS)
Non-contrast computerized tomography
Deep learning
Convolutional neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158223001304

Internet

http://www.sciencedirect.com/science/article/pii/S2213158223001304

Similar Items