COVID-19 Radiography Using ConvNets
4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022
; : 407-411, 2022.
Article
in English
| Scopus | ID: covidwho-2192071
ABSTRACT
The COVID-19 pandemic continues to have a negative impact on the fitness and well being of the worldwide population. A vital step in tackling the COVID-19 is a successful screening of patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aims to automatically identify patients with COVID-19 pneumonia using digital x-ray images of the chest while increasing the accuracy of the diagnosis using Convolution Neural networks (CNN). The data-set consists of 5380 X-ray images consisting of 1345 X-ray images each of COVID patients, Lung Opacity, Normal patients and Viral Pneumonia. In this study, CNN based model have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiography and gives a classification accuracy of 93.77% (training accuracy of 99.81% and validation accuracy of 95.45%). © 2022 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022
Year:
2022
Document Type:
Article
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