About: Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19

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About: Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19
Creator	Wang1, Bo Zeng1, Zihan Zhao2, Zhiwen
source	MedRxiv
abstract	In this research, an optimized deep learning method was proposed to explore the possibility and practicality of neural net-work applications in medical imaging. The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to optimize the Dropout module, which is essential in neural networks so as to improve the performance of typical neural network models. The experiment results demonstrate that the proposed method shows excellent performance and strong practicability in judging pneumonia, and the application of advanced artificial intelligence technology in the field of medical imaging has broad prospects.
has issue date	2020-04-26(xsd:dateTime)
bibo:doi	10.1101/2020.04.21.20072637
has license	medrxiv
sha1sum (hex)	38ee2b8fbf9dc2d506d5ae63d75d200fc6cdb65f
schema:url	https://doi.org/10.1101/2020.04.21.20072637
resource representing a document's title	Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19
resource representing a document's body	covid:38ee2b8fbf9dc2d506d5ae63d75d200fc6cdb65f#body_text
is schema:about of	named entity 'optimized' named entity 'Dropout' named entity 'performance' named entity 'RESEARCH' named entity 'explore' »more»

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