Uma Abordagem Para Detecção de Nódulos Pulmonares Baseada em Aprendizado Profundo

• Emerson Silva UFAL
• Luís França UFAL
• Marcelo Oliveira UFAL
• Fabiane Queiroz UFAL
• Rodolfo Cavalcante UFAL

DOI: https://doi.org/10.5753/sbcas.2020.11524

Abstract

Lung cancer is the kind of cancer which causes the higher number of deaths in the world. This justifies the need of a fast identification. The diagnosis of lung cancer is performed by specialists by means of computed tomography (CT) scans of the patients chest. This diagnosis is made in two important stages: (i) the detection of existing nodules and (ii) the classification of these nodules into malignant or benign, if they exist. In this work we investigated how to detect lung nodules in CT 2D by using convolutional neural networks (CNN) and principal component analyis (PCA). The main contribution of this work is a detection method applied to 2D CT scans which presents an accuracy similar to methods applied to 3D imagens, which are consequently more computationaly expensive. Results presented 86% of sensibility in identifying lung nodules.

References

Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhão, 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.

Bogot, N. R., Kazerooni, E. A., Kelly, A. M., Quint, L. E., Desjardins, B., and Nan, B. (2005). Interobserver and intrãobserver variability in the assessment of pulmonary nodule size on ct using film and computer display methods1. Academic Radiology, 12(8):948–956.

Brehéret, A. (2017). Pixel Annotation Tool. https://github.com/abreheret/ PixelAnnotationTool.

Cesar Uehara, Sérgio Jamnik, I. L. S. (1998). Cancer de Paulm ão .

Cesar Uehara, Sérgio Jamnik, I. L. S. E. L. B. W. V. d. S. (2008). Características clínicas, diagnosticas e laboratoriais de portadores de carcinoma bronquioloaveolar.

Chen, G., Zhang, J., Zhuo, D., Pan, Y., and Pang, C. (2019). Identification of pulmonary nodules via ct images with hierarchical fully convolutional networks. Medical & Biological Engineering & Computing, 57(7):1567–1580.

Chen, Y., Lin, Z., Zhão, X., Wang, G., and Gu, Y. (2014). Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2094–2107.

Ding, Jia Li, A. H. Z. W. L. (2019). Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10435 LNCS:559–567.

Dunteman, G. H. (1989). Principal components analysis. Number 69. Sage.

Eun, H., Kim, D., Jung, C., and Kim, C. (2018). Single-view 2d cnns with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection. Computer Methods and Programs in Biomedicine, 165:215 – 224.

Golan, R., Jacob, C., and Denzinger, J. (2016). Lung nodule detection in ct images using deep convolutional neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 243–250.

Gu, Y., Lu, X., Yang, L., Zhang, B., Yu, D., Zhão, Y., Gão, L., Wu, L., and Zhou, T. (2018). Automatic lung nodule detection using a 3d deep convolutional neural network combined with a multi-scale prediction strategy in chest cts. Computers in Biology and Medicine, 103:220 – 231.

Huang, Xiãojie Shan, J. V. V. (2017). Lung nodule detection in ct using 3d convolutional neural networks. pages 379–383.

Jiang, H., Ma, H., Qian, W., Gão, M., and Li, Y. (2018). An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE Journal of Biomedical and Health Informatics, 22(4):1227–1237.

Ker, J., Wang, L., Rão, J., and Lim, T. (2017). Deep learning applications in medical image analysis. Ieee Access, 6:9375–9389.

Lakshmanaprabu S.K., Sachi Nandan Mohanty, S. K. A. N. G. R. (2018). Optimal deep learning model for classification of lung cancer on ct images. Future Generation Computer Systems (2018).

Rakhlin, A., Shvets, A., Iglovikov, V., and Kalinin, A. A. (2018). Deep convolutional neural networks for breast cancer histology image analysis. In International Conference Image Analysis and Recognition, pages 737–744. Springer.

Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks.

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597.

Setio, A. A. A., Traverso, A., [de Bel], T., Berens, M. S., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B., van der Gugten, R., Heng, P. A., Jansen, B., [de Kaste], M. M., Kotov, V., Lin, J. Y.-H., Manders, J. T., Sonora-Mengana, A., García-Naranjo, J. C., Papavasileiou, E., Prokop, M., Saletta, M., Schaefer-Prokop, C. M., Scholten, E. T., Scholten, L., Snoeren, M. M., Torres, E. L., Vandemeulebroucke, J., Walasek, N., Zuidhof, G. C., van Ginneken, B., and Jacobs, C. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The luna16 challenge. Medical Image Analysis, 42:1 – 13.

Shen, D., Wu, G., and Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19:221–248.

Suki, B., Stamenovic, D., and Hubmayr, R. (2011). Lung Parenchymal Mechanics, pages 1317–1351. American Cancer Society.

Winkels, M. and Cohen, T. S. (2019). Pulmonary nodule detection in ct scans with equivariant cnns. Medical Image Analysis, 55:15 – 26.

Xie, H., Yang, D., Sun, N., Chen, Z., and Zhang, Y. (2019). Automated pulmonary nodule detection in ct images using deep convolutional neural networks. Pattern Recognition, 85:109 – 119.

Zhang, M., Zhang, L., and Cheng, H. (2010). A neutrosophic approach to image segmentation based on watershed method. Signal Processing, 90(5):1510 – 1517. Special Section on Statistical Signal Array Processing.