Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017 (2017), Article ID 4067832, 6 pages
https://doi.org/10.1155/2017/4067832
Research Article

Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

1School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi 470-1192, Japan
2School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake City, Aichi 470-1192, Japan
3Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan

Correspondence should be addressed to Atsushi Teramoto

Received 5 May 2017; Revised 20 June 2017; Accepted 5 July 2017; Published 13 August 2017

Academic Editor: Noriyoshi Sawabata

Copyright © 2017 Atsushi Teramoto 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. American Cancer Society, Cancer Facts and Figures 2015.
  2. P. Baas, J. S. A. Belderbos, S. Senan et al., “Concurrent chemotherapy (carboplatin, paclitaxel, etoposide) and involved-field radiotherapy in limited stage small cell lung cancer: a dutch multicenter phase II study,” British Journal of Cancer, vol. 94, no. 5, pp. 625–630, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. W. D. Travis, E. Brambilla, A. Burke, A. Marx, and A. G. Nicholson, Eds., WHO Classification of Tumours of the Lung, Pieura, Thymus, and Heart, IARC, Lyon, France, 2015.
  4. L. He, L. R. Long, S. Antani, and G. R. Thoma, “Histology image analysis for carcinoma detection and grading,” Computer Methods and Programs in Biomedicine, vol. 107, no. 3, pp. 538–556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Barker, A. Hoogi, A. Depeursinge, and D. L. Rubin, “Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles,” Medical Image Analysis, vol. 30, pp. 60–71, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. V. Ojansivu, N. Linder, E. Rahtu et al., “Automated classification of breast cancer morphology in histopathological images,” Diagnostic Pathology, vol. 8, no. 1, p. S29, 2013. View at Google Scholar
  7. L. Ficsor, V. S. Varga, A. Tagscherer, Z. Tulassay, and B. Molnar, “Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis,” Cytometry Part A, vol. 73A, no. 3, pp. 230–237, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1106–1114, 2012. View at Google Scholar
  9. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. View at Publisher · View at Google Scholar
  10. M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1207–1216, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Medical Physics, vol. 43, no. 4, pp. 1882–1886, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Teramoto, H. Fujita, O. Yamamuro, and T. Tamaki, “Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique,” Medical Physics, vol. 43, no. 6, pp. 2821–2827, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Miki, C. Muramatsu, T. Hayashi et al., “Classification of teeth in cone-beam CT using deep convolutional neural network,” Computers in Biology and Medicine, vol. 80, pp. 24–29, 2017. View at Publisher · View at Google Scholar
  14. D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, “Mitosis detection in breast cancer histology images with deep neural networks,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2011, vol. 8150 of Lecture Notes in Computer Science, pp. 411–418, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  15. H. Wang, A. Cruz-Roa, A. Basavanhally et al., “Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features,” Journal of Medical Imaging, vol. 1, no. 3, p. 034003, 2014. View at Publisher · View at Google Scholar
  16. M. G. Ertosun and D. L. Rubin, “Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks,” in Proceedings of the In AMIA Annual Symposium, pp. 1899–1908, 2015.
  17. J. Xu, X. Luo, G. Wang, H. Gilmore, and A. Madabhushi, “A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images,” Neurocomputing, vol. 191, pp. 214–223, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Litjens, C. I. Sánchez, N. Timofeeva et al., “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Scientific Reports, vol. 6, Article ID 26286, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Jia, E. Shelhamer, J. Donahue et al., “Caffe: convolutional architecture for fast feature embedding,” in Proceedings of the ACM International Conference on Multimedia, pp. 675–678, ACM, Orlando, FL, USA, November 2014. View at Publisher · View at Google Scholar
  20. R. Nizzoli, M. Tiseo, F. Gelsomino et al., “Accuracy of fine needle aspiration cytology in the pathological typing of non-small cell lung cancer,” Journal of Thoracic Oncology, vol. 6, no. 3, pp. 489–493, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. C. S. Sigel, A. L. Moreira, W. D. Travis et al., “Subtyping of non-small cell lung carcinoma: A comparison of small biopsy and cytology specimens,” Journal of Thoracic Oncology, vol. 6, no. 11, pp. 1849–1856, 2011. View at Publisher · View at Google Scholar · View at Scopus