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Lung Opacity Pneumonia Detection with Improved Residual Networks

Abstract

Purpose

Pneumonia detection is usually done by specialized highly trained specialists through the review of chest radiographs combined with vital signs, laboratory exams, and medical history. Though the presence of pneumonia in images appears in the form of areas of high opacity in the lungs which is difficult to differentiate from opaque areas caused by other conditions. The medical diagnosis of pneumonia is a very costly and time-consuming process. The motivation of this work is to automate the diagnosis process of pneumonia through image processing. In this paper, an automated pneumonia detection system is proposed using improved deep residual networks (ResNet) architectures, which are tested on the dataset of 30,227 DICOM Chest X-rays. Dataset was divided into 80% training and 20% testing, with 20% of the 80% used for training dedicated to validation.

Methods

Two residual network models were used (Version 1 & Version 2), and results were also compared with three different CNN models as well as methods found in recent literature.

Results

The overall results indicate that the proposed ResNet (Version 2) method achieves higher accuracy than convolution neural networks and other recently proposed methods (Table 5). The proposed ResNet network of a depth of 110 and a batch size of 16 with epochs 80 achieved an average accuracy of 88.67%.

Conclusion

An automated method is proposed and implemented in this work for the proper diagnosis of pneumonia using images of the DICOM chest X-rays dataset. The proposed method in this paper outperforms other methods from recent literature.

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Data availability

The dataset being used in this study is publicly published.

References

  1. 1.

    Watkins, K., & Sridhar, D. (2018). Pneumonia: A global cause without champions. The Lancet, 392(10149), 718–719.

    Article  Google Scholar 

  2. 2.

    Ye, X., Xiao, H., Chen, B., & Zhang, S. (2015). Accuracy of lung ultrasonography versus chest radiography for the diagnosis of adult community-acquired pneumonia: Review of the literature and meta-analysis. PLoS One10(6).

  3. 3.

    Nath, S. S., Mishra, G., Kar, J., Chakraborty, S., & Dey, N. (2014, July). A survey of image classification methods and techniques. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 554–557). IEEE.

  4. 4.

    Latif, G., Iskandar, D. A., Alghazo, J., & Jaffar, A. (2018). Improving brain MR image classification for tumor segmentation using phase congruency. Current Medical Imaging, 14(6), 914–922.

    Article  Google Scholar 

  5. 5.

    Butt, M. M., Latif, G., Iskandar, D. A., Alghazo, J., & Khan, A. H. (2019). Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images. Procedia Computer Science, 163, 283–291.

    Article  Google Scholar 

  6. 6.

    Khan, A. H., Latif, G., Iskandar, D. A., Alghazo, J., & Butt, M. (2018). Segmentation of melanoma skin lesions using anisotropic diffusion and adaptive thresholding. In Proceedings of the 2018 8th International Conference on Biomedical Engineering and Technology, pp. 39–45.

  7. 7.

    Latif, G., Iskandar, D. A., Alghazo, J. M., & Mohammad, N. (2018). Enhanced MR image classification using hybrid statistical and wavelets features. IEEE Access, 7, 9634–9644.

    Article  Google Scholar 

  8. 8.

    Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3462–3471. IEEE.

  9. 9.

    Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., & Lungren, M. P. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225

  10. 10.

    Noor, N. M., Rijal, O. M., Yunus, A., & Abu-Bakar, S. A. R. (2010). A discrimination method for the detection of pneumonia using chest radiograph. Computerized Medical Imaging and Graphics, 34(2), 160–166.

    Article  Google Scholar 

  11. 11.

    Xue, Z., You, D., Candemir, S., Jaeger, S., Antani, S., Long, L. R., & Thoma, G. R. Chest x-ray image view classification. In IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS), 2015 2015 pp. 66–71. IEEE.

  12. 12.

    Ko, B. C., Kim, S. H., & Nam, J. Y. (2011). X-ray image classification using random forests with local wavelet-based CS-local binary patterns. Journal of Digital Imaging, 24(6), 1141–1151.

    Article  Google Scholar 

  13. 13.

    Livieris, I., Kanavos, A., Tampakas, V., & Pintelas, P. (2018). An ensemble ssl algorithm for efficient chest x-ray image classification. Journal of Imaging, 4(7), 95.

  14. 14.

    Wang, C., Elazab, A., Wu, J., & Hu, Q. (2017). Lung nodule classification using deep feature fusion in chest radiography. Computerized Medical Imaging and Graphics, 57, 10–18.

    Article  Google Scholar 

  15. 15.

    Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574–582.

    Article  Google Scholar 

  16. 16.

    Kuruvilla, J., & Gunavathi, K. (2014). Lung cancer classification using neural networks for CT images. Computer methods and programs in biomedicine, 113(1), 202–209.

    Article  Google Scholar 

  17. 17.

    Cicero, M., Bilbily, A., Colak, E., Dowdell, T., Gray, B., Perampaladas, K., & Barfett, J. (2017). Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Investigative Radiology, 52(5), 281–287.

    Article  Google Scholar 

  18. 18.

    Farag, A., Ali, A., Graham, J., Farag, A., Elshazly, S., & Falk, R. (2011). Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. In IEEE international symposium on biomedical imaging: From nano to macro, pp. 169–172. IEEE.

  19. 19.

    Maduskar, P., Muyoyeta, M., Ayles, H., Hogeweg, L., Peters-Bax, L., & van Ginneken, B. (2013). Detection of tuberculosis using digital chest radiography: Automated reading vs. interpretation by clinical officers. The International Journal of Tuberculosis and Lung Disease, 17(12), 1613–1620.

  20. 20.

    Antani, S., & Candemir, S. (2015). Automated detection of lung diseases in chest X-rays. US National Library of Medicine.

  21. 21.

    Xu, T., Mandal, M., Long, R., Cheng, I., & Basu, A. (2012). An edge-region force guided active shape approach for automatic lung field detection in chest radiographs. Computerized Medical Imaging and Graphics, 36(6), 452–463.

    Article  Google Scholar 

  22. 22.

    Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. Chest pathology detection using deep learning with non-medical training. In ISBI, 2015, pp. 294–297.

  23. 23.

    Mubarok, A. F. A., Dominique, J. A. M., & Thias, A. H. (2019). Pneumonia detection with deep convolutional architecture. In 2019 International conference of artificial intelligence and information technology (ICAIIT), pp. 486–489. IEEE

  24. 24.

    Donthi, A, Huang, A., & Tammanagari, A. (2018). Detecting pneumonia with convolutional neural networks. Semanticscholar Org.: Allen Institute for Artificial Intelligence, Seattle, WA, USA

  25. 25.

    Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R., & Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & Electrical Engineering, 78, 388–399.

    Article  Google Scholar 

  26. 26.

    Tang, Y.-X., You-Bao, T., Yifan, P., Ke, Y., Bagheri, M., Redd, B. A., Brandon, C. J. (2020). Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digital Medicine, 3(1), pp. 1–8.

  27. 27.

    Wu, Z., Shen, C., & Van Den Hengel, A. (2019). Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recognition, 90, 119–133.

    Article  Google Scholar 

  28. 28.

    Shih, G., Wu, C. C., Halabi, S. S., Kohli, M. D., Prevedello, L. M., Cook, T. S., Sharma, A., Amorosa, J. K., Arteaga, V., Galperin-Aizenberg, M., & Gill, R. R. (2019). Augmenting the National Institutes of Health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1), e180041.

  29. 29.

    LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

    Article  Google Scholar 

  30. 30.

    O’Quinn, W., Haddad, R. J., & Moore, D. L. (2019). Pneumonia radiograph diagnosis utilizing deep learning network. In 2019 IEEE 2nd international conference on electronic information and communication technology (ICEICT), pp. 763–767. IEEE

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Acknowledgements

The authors would like to thank Dr. Runna Alghazo for proofreading and editing the manuscript to eliminate language issues and enhance readability wherever warranted.

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Correspondence to Ghazanfar Latif.

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Cite this article

Latif, G., Al Anezi, F.Y., Sibai, F.N. et al. Lung Opacity Pneumonia Detection with Improved Residual Networks. J. Med. Biol. Eng. (2021). https://doi.org/10.1007/s40846-021-00656-6

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Keywords

  • Lung opacity
  • Pneumonia detection
  • Residual networks
  • CNN
  • Chest X-ray