Multi-path convolutional neural network for lung cancer detection
- 51 Downloads
Lung cancer is the leading cause of death among cancer-related death. Like other cancers, the finest solution for lung cancer diagnosis and treatment is early screening. Automatic CAD system of lung cancer screening from Computed Tomography scan mainly involves two steps: detect all suspicious pulmonary nodules and evaluate the malignancy of the nodules. Recently, there are many works about the first step, but rare about the second step. Since the presence of pulmonary nodules does not absolutely specify cancer, the morphology of nodules such as shape, size, and contextual information has a sophisticated relationship with cancer, the screening of lung cancer needs a careful investigation on each suspicious nodule and integration of information of all nodules. We propose deep CNN architecture which differs from those traditionally used in computer vision to solve this problem. First, the suspicious nodules are generated with the modified version of U-Net and then the generated nodules become an input data for our model. The proposed model is a multi-path CNN which exploits both local features as well as more global contextual features simultaneously to automatically detect lung cancer. To this end, the model used three paths, each path employed different receptive field size which helps to model distant dependencies (short and long-range dependencies of the neighboring pixels). Then, to further upgrade our model performance, we concatenate features from the three paths. This balance the receptive field size effect and makes our model more adaptable to the variability of shape, size, and contextual information among nodules. Finally, we also introduce a retraining phase system that permits us to tackle difficulties related to the imbalance of image labels. Experimental results on Kaggle Data Science Bowl 2017 challenge shows that our model is better adaptable to the described inconsistency among nodules size and shape, and also obtained better detection results compared to the recently published state of the art methods.
KeywordsMedical image Image detection Lung cancer CNN’s
This work is partially funded by the MOE-Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804), the National Natural Science Foundation of China under Grant Nos. 61572155, 61672188 and 61272386 and Bule Hora University, Ethiopia. We would also like to acknowledge NVIDIA Corporation who kindly provided two sets of GPU. We would like to acknowledge the editors and the anonymous reviewers whose important comments and suggestions led to greatly improved the manuscript.
- Huang, X., Shan, J., & Vaidya, V. (2017). Lung nodules detection in CT using 3D Convolutional neural networks. In International Symposium on IEEE (pp. 379–383).Google Scholar
- Jarrett, K., et al., (2009). What is the best multi-stage architecture for object recognition? In Proceedings 12th International Conference on IEEE Computer Vision (pp. 2146–2153).Google Scholar
- Kaggle Data Science Bowl (KDSB). (2017). https://www.kaggle.com/c/data-science-bowl-2017/data. Accessed 25 Mar 2018.
- Kingsley, K., Mathieu, R., Gaurav, M., Huiling, C., Jie, L., Babar, N., et al. (2017). Deep learning for lung cancer detection: Tackling the Kaggle data science bowl 2017 challenge, arXiv preprint arXiv:1705.09435v1.
- Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Image Net classification with deep convolutional neural networks. In NIPS.Google Scholar
- Lepor, H. (2000). Prostatic diseases. Philadelphia: W.B Saunders Company.Google Scholar
- Lin, D. T., & Yan, C. R. (2002). Lung nodules identification rules extraction with neural fuzzy network. International Conference on in Neural Information Processing, 4, 20492053.Google Scholar
- Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., et al. (2017). Detecting cancer metastases on giga pixel pathology images, arXiv preprint arXiv:1703.02442.
- Lung Nodule Analysis (LUNA). (2016). https://luna16.grand-challenge.org/. Accessed 2 Mar 2018.
- Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier non linearity improve neural network acoustic models. In Proceedings of ICML (Vol. 30).Google Scholar
- Martin, A. et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467.
- Muhammad, R., Saeeda, N., & Ahmad, Z. (2017). Deep learning for medical image processing: Overview, challenges and future, arXiv preprint arXiv:1704.06825.
- Patz, E. F., Jr., Pinsky, P., Gatsonis, C., et al. (2014). Over diagnosis in low-dose computed tomography screening for lung cancer. Internal Medicine, 174(2), 269–274.Google Scholar
- Ramachandran, P., Zoph, B., & Le, Q. V. (2017). Searching for activation functions, arXiv preprint arXiv:1710.05941v2 [cs.NE].
- Rao, P., Pereira, N. A., & Srinivasan, R. (2016). Convolutional neural networks for lung cancer screening in computed tomography (CT) scans. In International conference on contemporary computing and informatics (pp. 489–493).Google Scholar
- Redmon, J., & Farhadi, A. (2016). Yolo: Better, faster, stronger, arXiv preprint arXiv:1612.08242.
- Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (pp. 91–99).Google Scholar
- WHO. (2018). http://www.who.int/news-room/fact-sheets/detail/cancer. Accessed 26 Oct 2018.