Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis
Abstract Background Coal workers’ pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution c...
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BMC
2025-01-01
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| Series: | BioMedical Engineering OnLine |
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| Online Access: | https://doi.org/10.1186/s12938-025-01333-4 |
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| author | Hantian Dong Biaokai Zhu Xiaomei Kong Xuesen Su Ting Liu Xinri Zhang |
| author_facet | Hantian Dong Biaokai Zhu Xiaomei Kong Xuesen Su Ting Liu Xinri Zhang |
| author_sort | Hantian Dong |
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| description | Abstract Background Coal workers’ pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future. Methods All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images. Results Four primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0. Conclusion We successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification. Trial registration: Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619 . |
| format | Article |
| id | doaj-art-33f685ceaf904deea91ae4c6803b9fc7 |
| institution | Kabale University |
| issn | 1475-925X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj-art-33f685ceaf904deea91ae4c6803b9fc72025-02-02T12:34:36ZengBMCBioMedical Engineering OnLine1475-925X2025-01-0124111810.1186/s12938-025-01333-4Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosisHantian Dong0Biaokai Zhu1Xiaomei Kong2Xuesen Su3Ting Liu4Xinri Zhang5First Department of Geriatric Diseases, First Hospital of Shanxi Medical UniversityNetwork Security Department, Shanxi Police College, Qingxu CountryDepartment of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical UniversityThe First College for Clinical Medicine, Shanxi Medical UniversityThe First College for Clinical Medicine, Shanxi Medical UniversityDepartment of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical UniversityAbstract Background Coal workers’ pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future. Methods All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient deep learning model and various image augmentation techniques (DenseNet-ECA). The classification performance of the different deep learning models was assessed, and receiver operating characteristic (ROC) curves and accuracy (ACC) were used to determine the optimal algorithm for classifying CWP clinical imaging features obtained from HRCT images. Results Four primary clinical imaging features in HRCT images, with a total of more than 1700 regions of interest (ROIs), were annotated, augmented, and used as a training set for tenfold cross-validation to generate the model. We selected DenseNet-Attention Net as the optimal model through assessing the performance of different classification algorithms, which yielded an average area under the ROC curve (AUC) of 0.98, and all clinical imaging features were classified with an AUC greater than 0.92. For the individual classifications, the AUCs were as follows: small miliary opacities, 0.99; nodular opacities, 1.0; interstitial changes, 0.92; and emphysema, 1.0. Conclusion We successfully applied a data augmentation strategy to develop a deep learning model by combining DenseNet with ECA-Net. We used our novel model to automatically classify CWP clinical imaging features from 2D HRCT images. This successful application of a deep learning-data augmentation algorithm can help clinical radiologists by providing reliable diagnostic information for classification. Trial registration: Chinese Clinical Trial Registry, ChiCTR2100050379. Registered on 27 August 2021, https://www.chictr.org.cn/bin/project/edit?pid=132619 .https://doi.org/10.1186/s12938-025-01333-4Coal workers’ pneumoconiosis classificationHigh-resolution computed tomographyDeep learningDenseNetECA-NetData augmentation |
| spellingShingle | Hantian Dong Biaokai Zhu Xiaomei Kong Xuesen Su Ting Liu Xinri Zhang Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis BioMedical Engineering OnLine Coal workers’ pneumoconiosis classification High-resolution computed tomography Deep learning DenseNet ECA-Net Data augmentation |
| title | Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis |
| title_full | Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis |
| title_fullStr | Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis |
| title_full_unstemmed | Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis |
| title_short | Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers’ pneumoconiosis |
| title_sort | deep learning based algorithm for classifying high resolution computed tomography features in coal workers pneumoconiosis |
| topic | Coal workers’ pneumoconiosis classification High-resolution computed tomography Deep learning DenseNet ECA-Net Data augmentation |
| url | https://doi.org/10.1186/s12938-025-01333-4 |
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