Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

• Giovanni da Silva UFMA
• Aristófanes Silva UFMA
• Anselmo de Paiva UFMA
• Marcelo Gattass PUC-Rio

DOI: https://doi.org/10.5753/sbcas.2016.9894

Resumo

Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

Referências

Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011). The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics, 38(2):915–931.

Browne, M. and Ghidary, S. S. (2003). Convolutional neural networks for image processing: an application in robot vision. In AI 2003: Advances in Artificial Intelligence, pages 641–652. Springer.

Carvalho Filho, A. O. d., de Sampaio, W. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2014). Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artificial intelligence in medicine, 60(3):165–177.

Dandil, E., Cakiroglu, M., Eksi, Z., Ozkan, M., Kurt, O. K., and Canan, A. (2014). Artificial neural network-based classification system for lung nodules on computed tomography scans. In Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of, pages 382–386. IEEE.

Gupta, B. and Tiwari, S. (2014). Lung cancer detection using curvelet transform and neural network. International Journal of Computer Applications, 86(1).

Hecht-Nielsen, R. (1989). Theory of the backpropagation neural network. In Neural Networks, 1989. IJCNN., International Joint Conference on, pages 593–605. IEEE.

Hua, K.-L., Hsu, C.-H., Hidayati, S. C., Cheng,W.-H., and Chen, Y.-J. (2015). Computeraided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8:2015–2022.

INCA (2016). Instituto nacional do câncer. tipos de câncer: Pulmão.http://www2.inca.gov.br/wps/wcm/connect/tiposdecancer/site/home/pulmao. Accessed: 2016-02-29.

Jabon, S. A., Raicu, D. S., and Furst, J. D. (2009). Content-based versus semantic-based retrieval: an lidc case study. In SPIE Medical Imaging, pages 72631L–72631L. International Society for Optics and Photonics.

Kang, K. and Wang, X. (2014). Fully convolutional neural networks for crowd segmentation. arXiv preprint arXiv:1411.4464.

Krewer, H., Geiger, B., Hall, L. O., Goldgof, D. B., Gu, Y., Tockman, M., and Gillies, R. J. (2013). Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pages 3887–3891. IEEE.

Kumar, D., Wong, A., and Clausi, D. A. (2015). Lung nodule classification using deep features in ct images. In Computer and Robot Vision (CRV), 2015 12th Conference on, pages 133–138. IEEE.

Kuruvilla, J. and Gunavathi, K. (2014). Lung cancer classification using neural networks for ct images. Computer methods and programs in biomedicine, 113(1):202–209.

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551.

LeCun, Y., Kavukcuoglu, K., Farabet, C., et al. (2010). Convolutional networks and applications in vision. In ISCAS, pages 253–256.

Leef, J. L. and Klein, J. S. (2002). The solitary pulmonary nodule. Radiologic Clinics of North America, 40(1):123–143.

Nascimento, L. B., de Paiva, A. C., and Silva, A. C. (2012). Lung nodules classification in ct images using shannon and simpson diversity indices and svm. In Machine Learning and Data Mining in Pattern Recognition, pages 454–466. Springer.

Orozco, H. M., Villegas, O. O. V., Dominguez, H. d. J. O., and Sanchez, V. G. C. (2013). Lung nodule classification in ct thorax images using support vector machines. The 11th International Conference on Information Sciences, Signal Processing and their Applications: Main Tracks, pages 277–283.

Parveen, S. S. and Kavitha, C. (2014). Classification of lung cancer nodules using svm kernels. International Journal of Computer Applications, 95(25).

Rubinstein, R. Y. and Kroese, D. P. (2013). The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning. Springer Science & Business Media.

Srichai, M. B., Naidich, D. P., Müller, N. L., and Webb, W. R. (2007). Computed tomography and magnetic resonance of the thorax. Lippincott Williams & Wilkins.