COVID-19 Classification from X-rays : A Comparative Study
3rd International Conference on Embedded and Distributed Systems, EDiS 2022
; : 75-80, 2022.
Article
in English
| Scopus | ID: covidwho-2223098
ABSTRACT
With the arrival of the most recent coronavirus pandemic, it was a must to find solutions to detect this dangerous virus. Analyzing X-ray images was among the exploited techniques to control this disease. However, the doctor's subjectivity in analyzing X-rays was the first obstacle in detecting this virus accurately. Applying new deep learning techniques to x-ray images can be a potential solution to reduce this subjectivity. This paper aims to conduct a comparative study between six different CNN architectures (VGG16, VGG19, Inception, Xception, DenseNet, and ChexNet) for COVID-19 detection from X-rays. The obtained results based on the transfer learning strategy confirm the efficiency of the VGG 16, where its achieved 98.69 % of accuracy on the COVID-19 Radiography Dataset. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
3rd International Conference on Embedded and Distributed Systems, EDiS 2022
Year:
2022
Document Type:
Article
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