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Journal of Healthcare Engineering
Volume 2017, Article ID 8314740, 7 pages
https://doi.org/10.1155/2017/8314740
Research Article

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

School of Computer Science & Software Engineering, Tianjin Polytechnics University, Tianjin, China

Correspondence should be addressed to Lei Zhao; moc.qq@7228596791

Received 10 March 2017; Accepted 14 May 2017; Published 9 August 2017

Academic Editor: Junfeng Gao

Copyright © 2017 QingZeng Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Key Statistics for Lung Cancer, https://www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html.
  2. A.C. Society, Cancer Facts and Figures, 2015, http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf.
  3. I. R. Valente, P. C. Cortez, E. C. Neto, J. M. Soares, V. H. de Albuquerque, and J. M. Tavares, “Automatic 3D pulmonary nodule detection in CT images: a survey,” Computer Methods and Programs in Biomedicine, vol. 124, no. 1, pp. 91–107, 2016. View at Google Scholar
  4. A. El-Baz, G. M. Beache, G. Gimel'farb et al., “Computer-aided diagnosis systems for lung cancer: challenges and methodologies review article,” International Journal of Biomedical Imaging, vol. 2013, Article ID 942353, 46 pages, 2013. View at Google Scholar
  5. H. Chen and W. WuH. Xia, J. Du, M. Yang, and B. Ma, “Classification of pulmonary nodules using neural network ensemble,” Advances in Neural Networks, Springer, Guilin, China, 2011. View at Publisher · View at Google Scholar
  6. J. Kuruvilla and K. Gunavathi, “Lung cancer classification using neural networks for CT images,” Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 202–209, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Krewer, B. Geiger, L. O. Hall et al., “Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 3887–3891, IEEE, Manchester, United Kingdom, 2013.
  8. D. Kumar, A. Wong, and D. A. Clausi, “Lung nodule classification using deep features in CT images,” in 12th Conference on Computer and Robot Vision (CRV), pp. 133–138, IEEE, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. K. L. Hua, C. H. Hsu, S. C. Hidayati, W. H. Cheng, and Y. J. Chen, “Computer-aided classification of lung nodules on computed tomography images via deep learning technique,” OncoTargets and Therapy, vol. 8, pp. 2015–2022, 2014. View at Google Scholar
  11. W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, “Multi-scale convolutional neural networks for lung nodule classification,” in Proceedings of 24th International Conference on Information Processing in Medical Imaging, pp. 588–599, 2015.
  12. H. C. Shin, H. R. Roth, M. Gao et al., “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. LIDC-IDRI, https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.
  14. M. Browne and S. S. Ghidary, “Convolutional neural networks for image processing: an application in robot vision,” in AI 2003: Advances in Artificial Intelligence, pp. 641–652, 2003.
  15. Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253–256, IEEE, 2010.
  16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, pp. 1097–1105, 2012.
  17. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014. View at Google Scholar
  19. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, “Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion,” Journal of Machine Learning Research, vol. 11, pp. 3371–3408, 2010. View at Google Scholar
  20. Y. Jia, E. Shelhamer, J. Donahue et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” in Proceedings of the 22nd ACM International Conference on Multimedia, ACM, 2014. View at Publisher · View at Google Scholar
  21. L. B. Nascimento, A. C. de Paiva, and A. C. Silva, “Lung nodules classification in CT images using Shannon and Simpson diversity indices and SVM,” in Machine Learning and Data Mining in Pattern Recognition, pp. 454–466, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. H. M. Orozco and O. O. V. Villegas, “Lung nodule classification in CT thorax images using support vector machines,” in 12th Mexican International Conference on Artificial Intelligence, pp. 277–283, IEEE, 2013.
  23. E. Dandıl, M. Çakiroğlu, Z. Ekşi, M. Özkan, Ö. K. Kurt, and A. Canan, “Artificial neural network-based classification system for lung nodules on computed tomography scans,” in 6th International Conference of Soft Computing and Pattern Recognition (soCPar), pp. 382–386, IEEE, 2014.
  24. S. S. Parveen and C. Kavitha, “Classification of lung cancer nodules using SVM kernels,” International Journal of Computer Applications, vol. 95, p. 25, 2014. View at Google Scholar
  25. B. Gupta and S. Tiwari, “Lung cancer detection using curvelet transform and neural network,” International Journal of Computer Applications, vol. 86, p. 1, 2014. View at Google Scholar
  26. G. L. F. da Silva, A. C. Silva, A. C. de Paiva, and M. Gattass, Classification of Malignancy of Lung Nodules in CT Images Using Convolutional Neural Network. View at Publisher · View at Google Scholar