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Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

  • Carlos A. FerreiraEmail author
  • António Cunha
  • Ana Maria Mendonça
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041.

Keywords

Texture classification Lung nodule 2.5D Deep learning 

Notes

Acknowledgments

This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness - COMPETE 2020 Programme and by the National Fundus through the Portuguese funding agency, FCT - Fundação para a Ciência e Tecnologia within project POCI-01-0145-FEDER-016673.

References

  1. 1.
    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
  2. 2.
    Callister, M.E.J., et al.: British thoracic society guidelines for the investigation and management of pulmonary nodules. Thorax 70(Suppl. 2), ii1–ii54 (2015)CrossRefGoogle Scholar
  3. 3.
    Ciompi, F., et al.: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci. Rep. 7, 46479 (2017)CrossRefGoogle Scholar
  4. 4.
    Cirujeda, P., et al.: 3d Riesz-wavelet based covariance descriptors for texture classification of lung nodule tissue in CT. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7909–7912. IEEE (2015)Google Scholar
  5. 5.
    Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)CrossRefGoogle Scholar
  6. 6.
    Henschke, C.I., et al.: Early lung cancer action project: overall design and findings from baseline screening. Lancet 354(9173), 99–105 (1999)CrossRefGoogle Scholar
  7. 7.
    Jacobs, C., et al.: Solid, part-solid, or non-solid? Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system. Invest. Radiol. 50(3), 168–173 (2015)CrossRefGoogle Scholar
  8. 8.
    McKee, B.J., Regis, S.M., McKee, A.B., Flacke, S., Wald, C.: Performance of ACR lung-RADS in a clinical ct lung screening program. J. Am. Coll. Radiol. 12(3), 273–276 (2015)CrossRefGoogle Scholar
  9. 9.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2018. CA: Cancer J. Clin. 68(1), 7–30 (2018)Google Scholar
  10. 10.
    Tu, X., et al.: Automatic categorization and scoring of solid, part-solid and non-solid pulmonary nodules in CT images with convolutional neural network. Sci. Rep. 7(1), 8533 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INESC-TEC - Institute for Systems and Computer Engineering, Technology and SciencePortoPortugal
  2. 2.University of Trás-os-Montes e Alto DouroVila RealPortugal
  3. 3.Faculty of EngineeringUniversity of PortoPortoPortugal

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