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.
deep learning; imaging detection; VGG-16 network; X-ray image; Image enhancement; Learning algorithms; Learning systems; Medical imaging; Signal detection; Structural optimization; Detection algorithm; Image detection; Imaging detections; Learning network; Network parameters; Network structures; X-ray detections; COVID-19
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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