Automated abnormality classification of chest radiographs using deep convolutional neural networks

Abstract As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiolo...

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Autores principales:	Yu-Xing Tang, You-Bao Tang, Yifan Peng, Ke Yan, Mohammadhadi Bagheri, Bernadette A. Redd, Catherine J. Brandon, Zhiyong Lu, Mei Han, Jing Xiao, Ronald M. Summers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/f5174425a0734861a7a45f22e97dfdd0
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spelling	oai:doaj.org-article:f5174425a0734861a7a45f22e97dfdd02021-12-02T16:50:27ZAutomated abnormality classification of chest radiographs using deep convolutional neural networks10.1038/s41746-020-0273-z2398-6352https://doaj.org/article/f5174425a0734861a7a45f22e97dfdd02020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0273-zhttps://doaj.org/toc/2398-6352Abstract As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45%, a sensitivity of 96.50 ± 0.36% and a specificity of 92.86 ± 0.48%) for normal versus abnormal chest radiograph classification. The CNN model obtained an AUC of 0.9804 ± 0.0032 (with an accuracy of 94.71 ± 0.32%, a sensitivity of 92.20 ± 0.34% and a specificity of 96.34 ± 0.31%) for normal versus lung opacity classification. Classification performance on the external dataset showed that the CNN model is likely to be highly generalizable, with an AUC of 0.9444 ± 0.0029. The CNN model pre-trained on cohorts of adult patients and fine-tuned on pediatric patients achieved an AUC of 0.9851 ± 0.0046 for normal versus pneumonia classification. Pretraining with natural images demonstrates benefit for a moderate-sized training image set of about 8500 images. The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.Yu-Xing TangYou-Bao TangYifan PengKe YanMohammadhadi BagheriBernadette A. ReddCatherine J. BrandonZhiyong LuMei HanJing XiaoRonald M. SummersNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Yu-Xing Tang You-Bao Tang Yifan Peng Ke Yan Mohammadhadi Bagheri Bernadette A. Redd Catherine J. Brandon Zhiyong Lu Mei Han Jing Xiao Ronald M. Summers Automated abnormality classification of chest radiographs using deep convolutional neural networks
description	Abstract As one of the most ubiquitous diagnostic imaging tests in medical practice, chest radiography requires timely reporting of potential findings and diagnosis of diseases in the images. Automated, fast, and reliable detection of diseases based on chest radiography is a critical step in radiology workflow. In this work, we developed and evaluated various deep convolutional neural networks (CNN) for differentiating between normal and abnormal frontal chest radiographs, in order to help alert radiologists and clinicians of potential abnormal findings as a means of work list triaging and reporting prioritization. A CNN-based model achieved an AUC of 0.9824 ± 0.0043 (with an accuracy of 94.64 ± 0.45%, a sensitivity of 96.50 ± 0.36% and a specificity of 92.86 ± 0.48%) for normal versus abnormal chest radiograph classification. The CNN model obtained an AUC of 0.9804 ± 0.0032 (with an accuracy of 94.71 ± 0.32%, a sensitivity of 92.20 ± 0.34% and a specificity of 96.34 ± 0.31%) for normal versus lung opacity classification. Classification performance on the external dataset showed that the CNN model is likely to be highly generalizable, with an AUC of 0.9444 ± 0.0029. The CNN model pre-trained on cohorts of adult patients and fine-tuned on pediatric patients achieved an AUC of 0.9851 ± 0.0046 for normal versus pneumonia classification. Pretraining with natural images demonstrates benefit for a moderate-sized training image set of about 8500 images. The remarkable performance in diagnostic accuracy observed in this study shows that deep CNNs can accurately and effectively differentiate normal and abnormal chest radiographs, thereby providing potential benefits to radiology workflow and patient care.
format	article
author	Yu-Xing Tang You-Bao Tang Yifan Peng Ke Yan Mohammadhadi Bagheri Bernadette A. Redd Catherine J. Brandon Zhiyong Lu Mei Han Jing Xiao Ronald M. Summers
author_facet	Yu-Xing Tang You-Bao Tang Yifan Peng Ke Yan Mohammadhadi Bagheri Bernadette A. Redd Catherine J. Brandon Zhiyong Lu Mei Han Jing Xiao Ronald M. Summers
author_sort	Yu-Xing Tang
title	Automated abnormality classification of chest radiographs using deep convolutional neural networks
title_short	Automated abnormality classification of chest radiographs using deep convolutional neural networks
title_full	Automated abnormality classification of chest radiographs using deep convolutional neural networks
title_fullStr	Automated abnormality classification of chest radiographs using deep convolutional neural networks
title_full_unstemmed	Automated abnormality classification of chest radiographs using deep convolutional neural networks
title_sort	automated abnormality classification of chest radiographs using deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f5174425a0734861a7a45f22e97dfdd0
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Automated abnormality classification of chest radiographs using deep convolutional neural networks

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