Cargando…
SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases
Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in t...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545283/ https://www.ncbi.nlm.nih.gov/pubmed/34976559 http://dx.doi.org/10.1109/ACCESS.2020.3032288 |
Ejemplares similares
-
Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs
Publicado: (2020) -
DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach
Publicado: (2020) -
A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data
Publicado: (2021) -
Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter
Publicado: (2020) -
CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter
Publicado: (2021)