PropertyValue
?:abstract
  • To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL), a flexible module to ensemble several convolutional neural network (CNN) models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score with 6% relative increase on Covid-19 identification task where it achieves 0.7833(0.07) in Precision, 0.8609(0.03) in Recall, and 0.8168(0.03) F1 score.
is ?:annotates of
?:arxiv_id
  • 2010.16039
?:creator
?:externalLink
?:license
  • arxiv
?:pdf_json_files
  • document_parses/pdf_json/6b065430c5caf929c9ea803cc5806be114029d50.json
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • ArXiv
?:title
  • FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
?:type
?:year
  • 2020-10-30

Metadata

Anon_0  
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/net/provenance/ns#DataItem>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2004/03/trix/rdfg-1/Graph>
<http://xmlns.com/foaf/0.1/primaryTopic> <https://research.tib.eu/covid-19/entity/jemz70bt>
<http://xmlns.com/foaf/0.1/topic> Anon_0
<http://www.ontologydesignpatterns.org/cp/owl/informationrealization.owl#realizes> <https://research.tib.eu/covid-19/data/entity/jemz70bt>
<http://purl.org/net/provenance/ns#createdBy>
Anon_1   (more)
expand all