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

SARS-CoV-2 has caused a severe pandemic worldwide. This virus appeared at the end of 2019. This virus causes respiratory distress syndrome. Computed tomography (CT) imaging provides important radiological information in the diagnosis and clinical evaluation of pneumonia caused by bacteria or a virus...

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Authors: Yaşar, Hüseyin, Ceylan, Murat, Cebeci, Hakan, Kılınçer, Abidin, Seher, Nusret, Kanat, Fikret, Koplay, Mustafa
Format: article
Publication Date:2023
Country:España
Institution:Universidad de Salamanca (USAL)
Repository:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/160205
Online Access:http://hdl.handle.net/10366/160205
Access Level:Open access
Keyword:computed tomography (CT)
convolutional neural network (CNN)
COVID-19, deep learning
severity
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spelling A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline AlgorithmYaşar, HüseyinCeylan, MuratCebeci, HakanKılınçer, AbidinSeher, NusretKanat, FikretKoplay, Mustafacomputed tomography (CT)convolutional neural network (CNN)COVID-19, deep learningseveritySARS-CoV-2 has caused a severe pandemic worldwide. This virus appeared at the end of 2019. This virus causes respiratory distress syndrome. Computed tomography (CT) imaging provides important radiological information in the diagnosis and clinical evaluation of pneumonia caused by bacteria or a virus. CT imaging is widely utilized in the identification and evaluation of COVID-19. It is an important requirement to establish diagnostic support systems using artificial intelligence methods to alleviate the workload of healthcare systems and radiologists due to the disease. In this context, an important study goal is to determine the clinical severity of the pneumonia caused by the disease. This is important for determining treatment procedures and the follow-up of a patient's condition. In the study, automatic COVID-19 severity classification was performed using three-class (mild, moderate, and severe) and two-class (non-severe and severe). In the study, deep learning models were used for classification. Also, CT images were utilized as radiological images. A total of 483 COVID-19 CT-image slices, 267 mild, 156 moderate, and 60 severe, were used. These images and labels were used directly for the three classifications. In the two-class classification, the mild and moderate images were accepted as non-severe. A total of eight classifications were made with convolutional neural network (CNN) architectures. These architectures are MobileNetv2, ResNet101, Xception, Inceptionv3, GoogleNet, EfficientNetb0, DenseNet201, and DarkNet53. In the study, the results of the top four CNN architectures with the best performance were combined using a pipeline algorithm. In this way, it is seen that significant improvements have been achieved in the results of the study. Before using the pipeline algorithm for the three-class classification, the results of weighted recall-sensitivity (SNST), specificity (SPCF), accuracy (ACCR), F-1 score (F-1), area under the receiver operating characteristic curve (AUC), and overall ACCR were obtained: 0.7785, 0.8351, 0.8299, 0.7758, 0.9112 and 0.7785, respectively. After using the pipeline algorithm for the three-class classification, the results of these parameters were obtained: 0.8095, 0.8555, 0.8563, 0.8076, 0.9089, and 0.8095, respectively. Before using the pipeline algorithm for the two-class classification, the results of SNST, SPCF, ACCR, F-1, and AUC were obtained: 0.9740, 0.8500, 0.9482, 0.9703, and 0.9788, respectively. After using the pipeline algorithm for the two-class classification, the results of these parameters were obtained: 0.9811, 0.8333, 0.9627, 0.9788, and 0.9851, respectively.Ediciones Universidad de Salamanca (España)202420242023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10366/160205reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1602052026-06-07T06:28:51Z
dc.title.none.fl_str_mv A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
title A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
spellingShingle A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
Yaşar, Hüseyin
computed tomography (CT)
convolutional neural network (CNN)
COVID-19, deep learning
severity
title_short A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
title_full A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
title_fullStr A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
title_full_unstemmed A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
title_sort A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
dc.creator.none.fl_str_mv Yaşar, Hüseyin
Ceylan, Murat
Cebeci, Hakan
Kılınçer, Abidin
Seher, Nusret
Kanat, Fikret
Koplay, Mustafa
author Yaşar, Hüseyin
author_facet Yaşar, Hüseyin
Ceylan, Murat
Cebeci, Hakan
Kılınçer, Abidin
Seher, Nusret
Kanat, Fikret
Koplay, Mustafa
author_role author
author2 Ceylan, Murat
Cebeci, Hakan
Kılınçer, Abidin
Seher, Nusret
Kanat, Fikret
Koplay, Mustafa
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv computed tomography (CT)
convolutional neural network (CNN)
COVID-19, deep learning
severity
topic computed tomography (CT)
convolutional neural network (CNN)
COVID-19, deep learning
severity
description SARS-CoV-2 has caused a severe pandemic worldwide. This virus appeared at the end of 2019. This virus causes respiratory distress syndrome. Computed tomography (CT) imaging provides important radiological information in the diagnosis and clinical evaluation of pneumonia caused by bacteria or a virus. CT imaging is widely utilized in the identification and evaluation of COVID-19. It is an important requirement to establish diagnostic support systems using artificial intelligence methods to alleviate the workload of healthcare systems and radiologists due to the disease. In this context, an important study goal is to determine the clinical severity of the pneumonia caused by the disease. This is important for determining treatment procedures and the follow-up of a patient's condition. In the study, automatic COVID-19 severity classification was performed using three-class (mild, moderate, and severe) and two-class (non-severe and severe). In the study, deep learning models were used for classification. Also, CT images were utilized as radiological images. A total of 483 COVID-19 CT-image slices, 267 mild, 156 moderate, and 60 severe, were used. These images and labels were used directly for the three classifications. In the two-class classification, the mild and moderate images were accepted as non-severe. A total of eight classifications were made with convolutional neural network (CNN) architectures. These architectures are MobileNetv2, ResNet101, Xception, Inceptionv3, GoogleNet, EfficientNetb0, DenseNet201, and DarkNet53. In the study, the results of the top four CNN architectures with the best performance were combined using a pipeline algorithm. In this way, it is seen that significant improvements have been achieved in the results of the study. Before using the pipeline algorithm for the three-class classification, the results of weighted recall-sensitivity (SNST), specificity (SPCF), accuracy (ACCR), F-1 score (F-1), area under the receiver operating characteristic curve (AUC), and overall ACCR were obtained: 0.7785, 0.8351, 0.8299, 0.7758, 0.9112 and 0.7785, respectively. After using the pipeline algorithm for the three-class classification, the results of these parameters were obtained: 0.8095, 0.8555, 0.8563, 0.8076, 0.9089, and 0.8095, respectively. Before using the pipeline algorithm for the two-class classification, the results of SNST, SPCF, ACCR, F-1, and AUC were obtained: 0.9740, 0.8500, 0.9482, 0.9703, and 0.9788, respectively. After using the pipeline algorithm for the two-class classification, the results of these parameters were obtained: 0.9811, 0.8333, 0.9627, 0.9788, and 0.9851, respectively.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/160205
url http://hdl.handle.net/10366/160205
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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