A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm

Hüseyin Yaşar; Murat Ceylan; Hakan Cebeci; Abidin Kılınçer; Nusret Seher; Fikret Kanat; Mustafa Koplay

doi:10.14201/adcaij.28715

A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm

Hüseyin Yaşar

Ministry of Health of Republic of Turkey, Ankara, Turkey.
Murat Ceylan

Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey.
Hakan Cebeci

Department of Radiology, Selçuk University Faculty of Medicine, Konya, Turkey.
Abidin Kılınçer

Department of Radiology, Selçuk University Faculty of Medicine, Konya, Turkey.
Nusret Seher

Department of Radiology, Selçuk University Faculty of Medicine, Konya, Turkey.
Fikret Kanat

Department of Chest Diseases, Selçuk University Faculty of Medicine, Konya, Turkey.
Mustafa Koplay

Department of Radiology, Selçuk University Faculty of Medicine, Konya, Turkey.

https://doi.org/10.14201/adcaij.28715

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How to Cite

Yaşar, H., Ceylan, M., Cebeci, H., Kılınçer, A., Seher, N., Kanat, F., & Koplay, M. (2023). A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e28715. https://doi.org/10.14201/adcaij.28715

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Editorial dates

Published:

29-12-2023

Issue

Vol. 12 (2023)

Section

Articles

Keywords

computed tomography (CT)
convolutional neural network (CNN)
COVID-19, deep learning
severity

Supporting agencies

This research didn't have any funding

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.