Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method

Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was dev...

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Auteurs principaux:	Yao-Mei Chen, Yenming J. Chen, Wen-Hsien Ho, Jinn-Tsong Tsai
Format:	article
Langue:	EN
Publié:	BMC 2021
Sujets:	COVID-19 Chest computed tomography image Convolutional neural network Algorithm hyperparameter Ensemble model Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Accès en ligne:	https://doaj.org/article/5ecac0b838334af486386455fe7e5523
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spelling	oai:doaj.org-article:5ecac0b838334af486386455fe7e55232021-11-14T12:13:02ZClassifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method10.1186/s12859-021-04083-x1471-2105https://doaj.org/article/5ecac0b838334af486386455fe7e55232021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04083-xhttps://doaj.org/toc/1471-2105Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models. Conclusions The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.Yao-Mei ChenYenming J. ChenWen-Hsien HoJinn-Tsong TsaiBMCarticleCOVID-19Chest computed tomography imageConvolutional neural networkAlgorithm hyperparameterEnsemble modelComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Chest computed tomography image Convolutional neural network Algorithm hyperparameter Ensemble model Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	COVID-19 Chest computed tomography image Convolutional neural network Algorithm hyperparameter Ensemble model Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Yao-Mei Chen Yenming J. Chen Wen-Hsien Ho Jinn-Tsong Tsai Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
description	Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models. Conclusions The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.
format	article
author	Yao-Mei Chen Yenming J. Chen Wen-Hsien Ho Jinn-Tsong Tsai
author_facet	Yao-Mei Chen Yenming J. Chen Wen-Hsien Ho Jinn-Tsong Tsai
author_sort	Yao-Mei Chen
title	Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
title_short	Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
title_full	Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
title_fullStr	Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
title_full_unstemmed	Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
title_sort	classifying chest ct images as covid-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
publisher	BMC
publishDate	2021
url	https://doaj.org/article/5ecac0b838334af486386455fe7e5523
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_version_	1718429348204118016

Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method

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