PropertyValue
?:abstract
  • The objective of the paper is to provide a model capable of serving as a basis for retraining a convolutional neural network that can be used to detect COVID-19 cases through spectrograms of coughing, sneezing and other respiratory sounds from infected people To address this challenge, the methodology was focused on Deep Learning technics worked with a dataset of sounds of sick and non-sick people, and using ImageNet\'s Xception architecture to train the model to be presented through Fine-Tuning The results obtained were a precision of 0 75 to 0 80, this being drastically affected by the quality of the dataset at our availability, however, when getting relatively high results for the conditions of the data used, we can conclude that the model can present much better results if it is working with a dataset specifically of respiratory sounds of COVID-19 cases with high quality © 2020 IEEE
is ?:annotates of
?:creator
?:journal
  • Proc._-_Int._Conf._Comput._Intell._Commun._Networks,_CICN
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • Deep Learning Audio Spectrograms Processing to the Early COVID-19 Detection
?:type
?:who_covidence_id
  • #960705
?:year
  • 2020

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/739u1or7>
<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/739u1or7>
<http://purl.org/net/provenance/ns#createdBy>
Anon_1   (more)
expand all