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COVID-19 X-RAY IMAGE DETECTION ALGORITHM BASED ON DEEP LEARNING
3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022 ; 12610, 2023.
Article Dans Anglais | Scopus | ID: covidwho-2327251
ABSTRACT
In order to enhance the ability to diagnose and distinguish COVID-19 from ordinary pneumonia, and to assist medical staff in chest X-ray detection of pneumonia patients, this paper proposes a COVID-19 X-ray image detection algorithm based on deep learning network. First of all, a model of deep learning network is set up based on VGG - 16, and then, the network structure and parameter optimization is adjusted, which makes the network model can be applied to COVID - 19 x ray imaging detection task. In the end, through adjusting the image size of the original data set, the input data meets the requirements of the deep learning network. Experimental results show that the proposed algorithm can effectively learn the characteristics of the COVID-19 X-ray image data set and accurately detect three states of COVID-19, common viral pneumonia and non-pneumonia, with a very high detection accuracy of 95.8%. © 2023 SPIE.
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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: Scopus langue: Anglais Revue: 3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022 Année: 2023 Type de document: Article

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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: Scopus langue: Anglais Revue: 3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022 Année: 2023 Type de document: Article