A Fast Automatic Juxta-pleural Lung Nodule Detection Framework Using Convolutional Neural Networks and Vote Algorithm
Abstract
Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.
Keywords
Lung cancer Juxta-pleural nodule detection Deep learning Weakly labeledReferences
- 1.American Cancer Society: Key statistics for lung cancer (2016)Google Scholar
- 2.Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)CrossRefGoogle Scholar
- 3.Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
- 4.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
- 5.Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
- 6.Li, R., Zeng, T., Peng, H., Ji, S.: Deep learning segmentation of optical microscopy images improves 3-D neuron reconstruction. IEEE Trans. Med. Imaging 36(7), 1533–1541 (2017)CrossRefGoogle Scholar
- 7.Cha, K.H., et al.: Urinary bladder segmentation in ct urography using deep-learning convolutional neural network and level sets. Med. Phys. 43(4), 1882–1896 (2016)CrossRefGoogle Scholar
- 8.Kumar, D., Wong, A., Clausi, D.A.: Lung nodule classification using deep features in ct images. In: 2015 12th Conference on Computer and Robot Vision (CRV), pp. 133–138. IEEE (2015)Google Scholar
- 9.Tajbakhsh, N., Suzuki, K.: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNS vs. CNNS. Pattern Recognit. 63, 476–486 (2017)CrossRefGoogle Scholar
- 10.Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic modelsGoogle Scholar
- 11.Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)