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Effect of Data Augmentation and Lung Mask Segmentation for Automated Chest Radiograph Interpretation of Some Lung Diseases

  • Peng Gang
  • Wei Zeng
  • Yuri GordienkoEmail author
  • Yuriy Kochura
  • Oleg Alienin
  • Oleksandr Rokovyi
  • Sergii Stirenko
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1142)

Abstract

The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions of some lung diseases by computer-aided diagnosis (CADx) based on the convolutional neural network (CNN) are presented for the largest open CXR dataset with radiologist-labeled reference standard evaluation sets (CheXpert). The results demonstrate the lower validation loss and higher area under curve (AUC) values for the receiver operating characteristic curve (ROC) for the models with lung mask segmentation (for 4 from 14 lung diseases) and data augmentation (for 10 from 14 lung diseases) for small image sizes (\(320\times 320\) pixels) and standard CNN (like DenseNet121) even. Moreover, the additional training leads to the lower validation loss and higher AUC values for the model with data augmentation. The further progress of CADx is assumed to be obtained for the big datasets with the bigger original image sizes by longer training with the tuned data augmentation.

Keywords

Deep learning Convolutional neural network Segmentation Data augmentation Chest X-ray Computer-aided diagnosis 

References

  1. 1.
    Shen, D., Wu, G., Suk, H.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRefGoogle Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Smistad, E., et al.: Medical image segmentation on GPUs - a comprehensive review. Med. Image Anal. 20(1), 1–18 (2015)CrossRefGoogle Scholar
  4. 4.
    Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
  5. 5.
    Irvin, J., et al.: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv preprint arXiv:1901.07031 (2019)
  6. 6.
    Gordienko, Y., et al.: Deep Learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 638–647. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-91008-6_63CrossRefGoogle Scholar
  7. 7.
    Peng, G., et al.: Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer. In: Proceedings of 10th International Conference on Advanced Computational Intelligence, ICACI 2018, pp. 878–883. IEEE (2018)Google Scholar
  8. 8.
    Stirenko, S., et al.: Chest X-ray analysis of tuberculosis by deep learning with segmentation and augmentation. In: Proceedings of IEEE 38th International Conference on Electronics and Nanotechnology, pp. 422–428. IEEE (2018)Google Scholar
  9. 9.
    Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174, 71–74 (2000)CrossRefGoogle Scholar
  10. 10.
    Armato, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)CrossRefGoogle Scholar
  11. 11.
    Jaeger, S., et al.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475–477 (2014)Google Scholar
  12. 12.
    Wang, X., et al.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097–2106. IEEE (2017). arXiv preprint arXiv:1705.02315
  13. 13.
    van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med. Image Anal. 10(1), 19–40 (2006)CrossRefGoogle Scholar
  14. 14.
    Hashemi, A., Pilevar, A.H.: Mass detection in lung CT images using region growing segmentation and decision making based on fuzzy systems. Int. J. Image Graph. Signal Process. 6(1), 1–8 (2013)CrossRefGoogle Scholar
  15. 15.
    Juhász, S., Horváth, Á., Nikházy, L., Horváth, G., Horváth, Á.: Segmentation of anatomical structures on chest radiographs. In: Bamidis, P.D., Pallikarakis, N. (eds.) XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. IFMBE Proceedings, vol. 29, pp. 359–362. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13039-7_90CrossRefGoogle Scholar
  16. 16.
    Kochura, Yu., Gordienko, Yu., Stirenko, S., et al.: Aggressive data augmentation and segmentation for lung disease diagnostics by deep learning (2019, submitted)Google Scholar
  17. 17.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  18. 18.
    Chollet, F.: Deep Learning with Python. Manning Publications, New York (2018)Google Scholar
  19. 19.
    Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint:1603.04467 (2016)Google Scholar
  20. 20.
    Gordienko, N., Lodygensky, O., Fedak, G., Gordienko, Yu.: Synergy of volunteer measurements and volunteer computing for effective data collecting, processing, simulating and analyzing on a worldwide scale. In: Proceedings of the IEEE 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 193–198. IEEE (2015)Google Scholar
  21. 21.
    Rather, N.N., Patel, C.O., Khan, S.A.: Using deep learning towards biomedical knowledge discovery. Int. J. Math. Sci. Comput. 3(2), 1–10 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peng Gang
    • 1
  • Wei Zeng
    • 1
  • Yuri Gordienko
    • 2
    Email author
  • Yuriy Kochura
    • 2
  • Oleg Alienin
    • 2
  • Oleksandr Rokovyi
    • 2
  • Sergii Stirenko
    • 2
  1. 1.Huizhou UniversityHuizhouChina
  2. 2.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KyivUkraine

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