Keywords:
deep learning, lung nodule classification, finetuning technique, feature selection
Among most popular feature extractors, pretrained deep neural networks play a central role in transfer learning to extract high-level feature on small datasets. The transferable performance, however, cannot be guaranteed for the task of interest. To enhance the transferability, this paper employs fine-tuning and feature selection in a different way to improve the accuracy of lung nodule classification. The fine-tuning technique retrains the neural network using lung nodule dataset, while feature selection captures a useful subset of features for lung nodule classification. Preliminary experimental results on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) confirm that the classification accuracy on lung nodule can be significantly improved via finetuning and feature selection. Furthermore, the results outperform competitively handcrafted texture descriptors.
- Hongming Shan
- Fudan University
- Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, USA
- Mannudeep K. Kalra
- Massachusetts General Hospital
- Rodrigo Canellas de Souza
- Massachusetts General Hospital
- Junping Zhang
- Fudan University
- N. L. S. T. R. Team et al., “Reduced lung-cancer mortality with lowdose computed tomographic screening,” The New England Journal of Medicine, vol. 2011, no. 365, pp. 395–409, 2011.
- H. J. Aerts, E. R. Velazquez, R. T. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature Communications, vol. 5, 2014.
- F. Han, G. Zhang, H. Wang, B. Song, H. Lu, D. Zhao, H. Zhao, and Z. Liang, “A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database,” in International Conference on Medical Imaging Physics and Engineering. IEEE, 2013, pp. 14–18.
- W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, “Multi-scale convolutional neural networks for lung nodule classification,” in International Conference on Information Processing in Medical Imaging. Springer, 2015, pp. 588–599.
- 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. IEEE, 2015, pp. 133–138.
- T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
- N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE, 2005, pp. 886–893.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
- J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in Neural Information Processing Systems, 2014, pp. 3320–3328.
- S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. , A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman et al., “The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans,” Medical physics, vol. 38, no. 2, pp. 915–931, 2011.
- A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: An astounding baseline for recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014, pp. 806–813.
- O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma,Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” Journal of Machine Learning Research, vol. 12, no. Oct, pp. 2825–2830, 2011.
- S. Van der Walt, J. L. Schonberger, J. Nunez-Iglesias, F. Boulogne, ¨J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “Scikit-image: Image processing in python,” PeerJ, vol. 2, p. e453, 2014.