Cargando…
Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images
BACKGROUND: Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological requirements. METHODS: A fully automatic pipeline...
Autores principales: | Qi, Qianqian, Qi, Shouliang, Wu, Yanan, Li, Chen, Tian, Bin, Xia, Shuyue, Ren, Jigang, Yang, Liming, Wang, Hanlin, Yu, Hui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715632/ https://www.ncbi.nlm.nih.gov/pubmed/34979404 http://dx.doi.org/10.1016/j.compbiomed.2021.105182 |
Ejemplares similares
-
DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images
por: Qi, Shouliang, et al.
Publicado: (2021) -
Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans
por: Wu, Yanan, et al.
Publicado: (2023) -
Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks
por: Hamwood, Jared, et al.
Publicado: (2019) -
Fully automatic wound segmentation with deep convolutional neural networks
por: Wang, Chuanbo, et al.
Publicado: (2020) -
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
por: Chlebus, Grzegorz, et al.
Publicado: (2018)