Cargando…
Contour-enhanced attention CNN for CT-based COVID-19 segmentation
Accurate detection of COVID-19 is one of the challenging research topics in today's healthcare sector to control the coronavirus pandemic. Automatic data-powered insights for COVID-19 localization from medical imaging modality like chest CT scan tremendously augment clinical care assistance. In...
Autores principales: | Karthik, R., Menaka, R., M, Hariharan, Won, Daehan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767763/ https://www.ncbi.nlm.nih.gov/pubmed/35068591 http://dx.doi.org/10.1016/j.patcog.2022.108538 |
Ejemplares similares
-
CT-based severity assessment for COVID-19 using weakly supervised non-local CNN
por: Karthik, R., et al.
Publicado: (2022) -
Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN
por: Karthik, R., et al.
Publicado: (2021) -
Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier
por: M, Malathi, et al.
Publicado: (2022) -
Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation
por: Rodriguez-Obregon, Diomar Enrique, et al.
Publicado: (2023) -
CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement †
por: Peng, Changmeng, et al.
Publicado: (2021)