COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
By Rodolfo Miranda Pereira, Diego Bertolini, Lucas O. Teixeira, Carlos Nascimento Silla Jr., Yandre M. G. Costa. Comput. Methods Programs Biomed., 2020
429 citations recorded by the source corpus.
Highlights • COVID-19 identification in chest X-ray using multi-class and hierarchical learners.• Using a database that reflects a real world scenario with its natural imbalance.• Exploring the textural content from the chest X-ray images with pneumonia.• Evaluating handcrafted and learned features to investigate its complementarities.
Subfields: Neurology, Periodontics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/c7b262f9564eebef3810b9a4a185b74a3291df61
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Radiology, Nuclear Medicine and Imaging
- Marzyeh Ghassemi
- Joseph Paul Cohen
- Ioannis D. Apostolopoulos
- Alexander Wong
- Linda Wang
- Deniz Korkmaz
- Ferhat Ucar
- Abolfazl Attar
Citations in the corpus
- Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Pneumonia is a type of infection that causes inflammaion in the lungs, and besides the SARS-CoV-2 virus, bacteria, fungi, nd other viruses often play a role in the emergence of this disase [2] .”
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “[27] Pereira RM, Bertolini D, Teixeira LO, Silla CN, Costa YMG.”
- COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, methodology): “Another work has utilized the pathology hierarchy we provide to improve predictions by using the Clus-HMC method [Pereira et al., 2020].”