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...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-05-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-020-0273-z |
| _version_ | 1797643164485419008 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T14:10:46Z |
| format | Article |
| id | doaj.art-f5174425a0734861a7a45f22e97dfdd0 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T14:10:46Z |
| publishDate | 2020-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-f5174425a0734861a7a45f22e97dfdd02023-11-02T00:22:06ZengNature Portfolionpj Digital Medicine2398-63522020-05-01311810.1038/s41746-020-0273-zAutomated abnormality classification of chest radiographs using deep convolutional neural networksYu-Xing Tang0You-Bao Tang1Yifan Peng2Ke Yan3Mohammadhadi Bagheri4Bernadette A. Redd5Catherine J. Brandon6Zhiyong Lu7Mei Han8Jing Xiao9Ronald M. Summers10Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterNational Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterClinical Image Processing Service, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterRadiology and Imaging Sciences, National Institutes of Health Clinical CenterDepartment of Radiology, University of MichiganNational Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthPAII IncPing An TechnologyImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical CenterAbstract 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.https://doi.org/10.1038/s41746-020-0273-z |
| spellingShingle | 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 npj Digital Medicine |
| title | 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_short | Automated abnormality classification of chest radiographs using deep convolutional neural networks |
| title_sort | automated abnormality classification of chest radiographs using deep convolutional neural networks |
| url | https://doi.org/10.1038/s41746-020-0273-z |
| work_keys_str_mv | AT yuxingtang automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT youbaotang automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT yifanpeng automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT keyan automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT mohammadhadibagheri automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT bernadettearedd automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT catherinejbrandon automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT zhiyonglu automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT meihan automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT jingxiao automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks AT ronaldmsummers automatedabnormalityclassificationofchestradiographsusingdeepconvolutionalneuralnetworks |