Abstract
Classification of bone tumor plays an important role in treatment. As artificial diagnosis is in low efficiency, an automatic classification system can help doctors analyze medical images better. However, most existing methods cannot reach high classification accuracy on clinical images because of the high similarity between images. In this paper, we propose a super label guided convolutional neural network (SG-CNN) to classify CT images of bone tumor. Images with two hierarchical labels would be fed into the network, and learned by its two sub-networks, whose tasks are learning the whole image and focusing on lesion area to learn more details respectively. To further improve classification accuracy, we also propose a multi-channel enhancement (ME) strategy for image preprocessing. Owing to the lack of suitable public dataset, we introduce a CT image dataset of bone tumor. Experimental results on this dataset show our SG-CNN and ME strategy improve the classification accuracy obviously.
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References
Zhang, Y.J., Cui, X.F., Li, C.C., Li, S.J.: Efficacy of DR, CT and MRI in bone tumors. Chinese-German J Clin. Oncol. 13(4), 181–184 (2014)
Keidar, Z., Israel, O., Krausz, Y.: SPECT/CT in tumor imaging: technical aspects and clinical applications. Semin. Nucl. Med. 33(3), 205 (2003)
Sánchez A, V.D.: Advanced support vector machines and kernel methods. Neurocomputing. 55(1), 5–20 (2003)
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., et al.: Dermatolo gist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. arXiv:1705.02315 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G. E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
Li, Q., Cai, W., Wang, X., Zhou, Y.: Medical image classification with convolutional neural network. In: International Conference on Control Automation Robotics and Vision, pp. 844–848. IEEE (2016)
Miki, Y., et al.: Classification of teeth in cone-beam CT using deep convolutional neural network. Comput. Biol. Med. 80(C), 24–29 (2017)
Kumar, P., Grewal, M., Srivastava, M.M.: Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs. arXiv:1711.08760 (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition (CVPR), pp. 248–255. IEEE (2009)
Wei, X.S., Xie, C.W., Wu, J.: Mask-CNN localizing parts and selecting descriptors for fine-grained image recognition. In: Conference and Workshop on Neural Information Processing Systems (NIPS) (2016). arXiv:1605.06878
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_54
Huang, S., Xu, Z., Tao, D., Zhang, Y.: Part-stacked CNN for fine-grained visual categorization. In: Computer Vision and Pattern Recognition, pp. 1173–1182. IEEE (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE Computer Society (2005)
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433–459 (2010)
Hoochang, S., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285 (2016)
Acknowledgements
This work was supported in part by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing), and by Shenzhen International cooperative research projects GJHZ20170313150021171.
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Li, Y., Zhou, W., Lv, G., Luo, G., Zhu, Y., Liu, J. (2018). Classification of Bone Tumor on CT Images Using Deep Convolutional Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_13
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