Cargando…
DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels
The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predi...
Autores principales: | Wu, Yunan, Dravid, Amil, Wehbe, Ramsey Michael, Katsaggelos, Aggelos K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215672/ https://www.ncbi.nlm.nih.gov/pubmed/37237626 http://dx.doi.org/10.3390/bioengineering10050556 |
Ejemplares similares
-
DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set
por: Wehbe, Ramsey M., et al.
Publicado: (2021) -
To FUSE(x) or not to FUSE(x) ...
por: Peters, Andreas Joachim
Publicado: (2020) -
Automated detection of Covid-19 disease using deep fused features from chest radiography images
por: Uçar, Emine, et al.
Publicado: (2021) -
Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning
por: Ji, Dongsheng, et al.
Publicado: (2021) -
A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays
por: Rajaraman, Sivaramakrishnan, et al.
Publicado: (2022)