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. 2021 Dec 8;21(1):189.
doi: 10.1186/s12880-021-00723-z.

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Fan Yang #  1   2 , Zhi-Ri Tang #  3   2 , Jing Chen  1 , Min Tang  1 , Shengchun Wang  4 , Wanyin Qi  1 , Chong Yao  2   5 , Yuanyuan Yu  2   5 , Yinan Guo  6 , Zekuan Yu  7   8   9   10
Affiliations

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Fan Yang et al. BMC Med Imaging. .

Abstract

Purpose: The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms.

Materials and methods: 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people.

Results: Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively.

Conclusion: The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

Keywords: Deep learning; Pneumoconiosis diagnosis; ResNet; U-Net; X-rays.

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Conflict of interest statement

The authors declares that they have no competing interests.

Figures

Fig. 1
Fig. 1
An overview of the method adopted in this work
Fig. 2
Fig. 2
An example of a original image and b that after lung region segmentation
Fig. 3
Fig. 3
The training and validation accuracy with epochs
Fig. 4
Fig. 4
The training and validation losses with epochs
Fig. 5
Fig. 5
The ROC curve
Fig. 6
Fig. 6
Confusion matrix of pneumoconiosis classification into four categories
See this image and copyright information in PMC

References

    1. Wang T, Sun W, Wu H, Cheng Y, Li Y, Meng F, Ni C. Respiratory traits and coal workers’ pneumoconiosis: Mendelian randomisation and association analysis. Occup Environ Med. 2021;78(2):137. doi: 10.1136/oemed-2020-106610. - DOI - PubMed
    1. Hall NB, Blackley DJ, Halldin CN, Laney AS. Current review of pneumoconiosis among US coal miners. Curr Environ Health Rep. 2019;6(3):137–147. doi: 10.1007/s40572-019-00237-5. - DOI - PMC - PubMed
    1. Blackley DJ, Halldin CN, Laney AS. Continued increase in prevalence of coal workers’ pneumoconiosis in the United States, 1970–2017. Am J Public Health. 2018;108(9):1220–1222. doi: 10.2105/AJPH.2018.304517. - DOI - PMC - PubMed
    1. PNEUMOCONIOSES O: GUIDELINES FOR THE USE OF THE ILO INTERNATIONAL CLASSIFICATION OF RADIOGRAPHS. 2002.
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539. - DOI - PubMed

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