On the Use of Conventional Neural Networks for COVID-19 Detection in CT-Scan Images: A Comparative Study and Performance Analysis
2022 IEEE International Conference on Big Data, Big Data 2022
; : 4410-4415, 2022.
Article
in English
| Scopus | ID: covidwho-2274297
ABSTRACT
This paper presents a comprehensive study on deep learning for COVID-19 detection using CT-scan images. The proposed study investigates several Conventional Neural Networks (CNN) architectures such as AlexNet, ZFNet, VGGNet, and ResNet, and thus proposed a hybrid methodology base on merging the relevant optimized architectures considered for detecting COVID-19 from CT-scan images. The proposed methods have been assessed on real datasets, and the experimental results conducted have shown the effectiveness of the proposed methods, allowing achieving a higher accuracy up to 99%. © 2022 IEEE.
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Database:
Scopus
Language:
English
Journal:
2022 IEEE International Conference on Big Data, Big Data 2022
Year:
2022
Document Type:
Article
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