Using Deep Learning for Classification of Early Lung Nodules on Computed Tomography Images
Resumo
Lung cancer is the leading cause of cancer mortality, accounting for approximately 20% of all cancer-related deaths. Nevertheless, despite the development of new therapeutic agents and technologies, only 16% of lung cancer patients are diagnosed at early stages. Therefore, to diagnose in early stages, when the nodules are very small, is a complex task for specialists and presents some challenges. To assist the specialists, the main purpose of this work is to propose the use of Deep Learning to classify 25,200 small pulmonary nodules balanced with diameter 5-10mm. The result was of 0.992 (+/- 0.001) of area under ROC curve using 10-fold cross validation. The proposed method showed to be promising to assist the specialists in classification of small lung nodules.
Referências
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Reeves, A. and Kostis., W. (2000). Computer-aided diagnosis of small pulmonary nodules. Seminars in Ultrasound, CT and MRI, 21(2):116–128.
Samuel G. Armato, Geoffrey McLennan, L. B. M. F. M., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C., Qing, D. P., Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., and Clarke, L. P. (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.
Song, Q., Zhao, L., Luo, X., and Dou, X. (2017). Using deep learning for classification of lung nodules on computed tomography images. In Journal of healthcare engineering.
Sonoda, S. and Murata, N. (2015). Neural network with unbounded activations is universal approximator. CoRR, abs/1505.03654.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958.
Tammemagi, M. C. and Lam, S. (2014). Screening for lung cancer using low dose computed tomography. BMJ, 348. PMID: 24865600.
Yankelevitz, D., Gupta, R., Zhao, B., and Henschke., C. (1999). Pulmonary nodule characterization, including computer analysis and quantitative features. Radiology, 212(2):561–566. PMID: 10429718.
Zappa, C. and Mousa, S. A. (2016). Non-small cell lung cancer: current treatment and future advances. Translational Lung Cancer Research, 5(3).
Zeiler, M. D. (2012). ADADELTA: an adaptive learning rate method. CoRR, abs/1212.5701.
Alilou, M., Kovalev, V., Snezhko, E., and Taimouri, V. (2014). A comprehensive framework for automatic detection of pulmonary nodules in lung ct images. Image Analysis Stereology, 33(1).
Anirudh, R., Thiagarajan, J. J., Bremer, T., and Kim, H. (2016). Lung nodule detection using 3d convolutional neural networks trained on weakly labeled data. In Medical Imaging: Computer-Aided Diagnosis.
Bartholmai, B., Koo, C., Johnson, G., White, D., Raghunath, S., Rajagopalan, S., Moynagh, M., Lindell, R., and Hartman, T. (2015). Pulmonary nodule characterization, including computer analysis and quantitative features. Journal of Thoracic Imaging, 30(2):139–156.
Blandin Knight, S., Crosbie, P. A., Balata, H., Chudziak, J., Hussell, T., and Dive, C. (2017). Progress and prospects of early detection in lung cancer. Open Biology, 7(9).
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., and Tang, A. (2017). Deep learning: A primer for radiologists. RadioGraphics, 37(7):2113–2131. PMID: 29131760.
Chollet, F. et al. (2015). Keras. [link].
Dilger, S. K. N., Uthoff, J., Judisch, A., Hammond, E., Smith, S. L. M. B. J., John D. Newell, J., Hoffman, E. A., and Sieren, J. C. (2015). Improved pulmonary nodule classification utilizing quantitative lung parenchyma features. Journal of Medical Imaging, 2(4):041004–1–041004–10.
Fchollet, Matsuyamax, Smerity, kemaswill, and Treszkai (2018). Keras examples mnist. [link].
Felix, A. d. L. F., Ferreira Junior, J. R., Oliveira, M. C., and Machado, A. P. (2016). Using 3d texture and margin sharpness features on classification of small pulmonary nodules. In Proceedings... Conference on Graphics, Patterns and Images, 29. (SIBGRAPI), IEEE Computer Society’s Conference Publishing Services.
Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016). Cloud-based nosql open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research. Journal of Digital Imaging, 29(6):716–729.
Gillies, R. J., Kinahan, P. E., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–577. PMID: 26579733.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding. Neurocomput., 187(C):27–48.
Hua K-L, Hsu C-H, H. S. C. W.-H. and Y-J, C. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8:2015–2022.
Kooi, T., Litjens, G., Ginneken, B., A. Gubern-Mérida, C., Sánchez, Mann, R., Den, H., and Karssemeijer, N. (2017). Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Analysis, 35:303–312.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521:436–444.
Litjens, G. J. S., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. CoRR, abs/1702.05747.
Ravı̀, D., Wong, C., Deligianni, F., Berthelot, M., Pérez, J. A., Lo, B. P. L., and Yang, G.-Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21:4–21.
Reeves, A. and Kostis., W. (2000). Computer-aided diagnosis of small pulmonary nodules. Seminars in Ultrasound, CT and MRI, 21(2):116–128.
Samuel G. Armato, Geoffrey McLennan, L. B. M. F. M., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C., Qing, D. P., Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., and Clarke, L. P. (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.
Song, Q., Zhao, L., Luo, X., and Dou, X. (2017). Using deep learning for classification of lung nodules on computed tomography images. In Journal of healthcare engineering.
Sonoda, S. and Murata, N. (2015). Neural network with unbounded activations is universal approximator. CoRR, abs/1505.03654.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958.
Tammemagi, M. C. and Lam, S. (2014). Screening for lung cancer using low dose computed tomography. BMJ, 348. PMID: 24865600.
Yankelevitz, D., Gupta, R., Zhao, B., and Henschke., C. (1999). Pulmonary nodule characterization, including computer analysis and quantitative features. Radiology, 212(2):561–566. PMID: 10429718.
Zappa, C. and Mousa, S. A. (2016). Non-small cell lung cancer: current treatment and future advances. Translational Lung Cancer Research, 5(3).
Zeiler, M. D. (2012). ADADELTA: an adaptive learning rate method. CoRR, abs/1212.5701.
Publicado
22/07/2018
Como Citar
LIMA, Lucas; OLIVEIRA, Marcelo.
Using Deep Learning for Classification of Early Lung Nodules on Computed Tomography Images. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 105-116.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3681.
