Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage...

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Autores principales:	Khin Yadanar Win, Noppadol Maneerat, Syna Sreng, Kazuhiko Hamamoto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 chest X-rays deep learning ensemble learning image augmentation oversampling Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/4cb34389d3d5420d848a1865b585639c
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spelling	oai:doaj.org-article:4cb34389d3d5420d848a1865b585639c2021-11-25T16:30:51ZEnsemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset10.3390/app1122105282076-3417https://doaj.org/article/4cb34389d3d5420d848a1865b585639c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10528https://doaj.org/toc/2076-3417The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.Khin Yadanar WinNoppadol ManeeratSyna SrengKazuhiko HamamotoMDPI AGarticleCOVID-19chest X-raysdeep learningensemble learningimage augmentationoversamplingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10528, p 10528 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 chest X-rays deep learning ensemble learning image augmentation oversampling Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	COVID-19 chest X-rays deep learning ensemble learning image augmentation oversampling Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
description	The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.
format	article
author	Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto
author_facet	Khin Yadanar Win Noppadol Maneerat Syna Sreng Kazuhiko Hamamoto
author_sort	Khin Yadanar Win
title	Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
title_short	Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
title_full	Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
title_fullStr	Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
title_full_unstemmed	Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
title_sort	ensemble deep learning for the detection of covid-19 in unbalanced chest x-ray dataset
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4cb34389d3d5420d848a1865b585639c
work_keys_str_mv	AT khinyadanarwin ensembledeeplearningforthedetectionofcovid19inunbalancedchestxraydataset AT noppadolmaneerat ensembledeeplearningforthedetectionofcovid19inunbalancedchestxraydataset AT synasreng ensembledeeplearningforthedetectionofcovid19inunbalancedchestxraydataset AT kazuhikohamamoto ensembledeeplearningforthedetectionofcovid19inunbalancedchestxraydataset
_version_	1718413134204502016

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

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