Classificação de Nódulos Pulmonares Utilizando Redes Neurais Convolucionais 3D
Resumo
O câncer de pulmão é o segundo mais comum no Brasil. A detecção precoce de nódulos pulmonares é essencial para a sobrevivência do paciente. Neste trabalho propomos um algoritmo baseado na Rede Neural Convolucional 3D para classificar nódulos pulmonares como benignos ou malignos em imagens da tomografia computadorizada. A arquitetura proposta possui dois blocos de camadas convolucionais seguidos por uma camada de pooling, duas camadas totalmente conectadas e uma softmax. O uso de aumento de dados para balanceamento do conjunto de treino produziu resultados promissores, com acurácia de 0,9077, kappa de 0,7774, sensibilidade de 0,8481, especificidade de 0,9322 e AUC de 0,8901, na classificação de nódulos pulmonares.
Referências
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da Nobrega, R. V. M. et al. (2018). Lung nodule classification via deep transfer learning in ct lung images. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pages 244–249.
de Carvalho Filho, A. O., 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.
de Carvalho Filho, A. O., , et al. (2017). Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and svm. Medical & Biological Engineering & Computing, 55(8):1129–1146.
Deep, G., Kaur, L., and Gupta, S. (2016). Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval. Engineering Science and Technology, an International Journal, 19(4):1895 – 1909.
Dey, R., Lu, Z., and Hong, Y. (2018). Diagnostic classification of lung nodules using 3d neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 774–778.
Inca (2019). Instituto Nacional do Cancer - minist erio da saúde, cáncer de pulm ao. disponível em: https://www.inca.gov.br/tipos-de-cancer/cancer-de-pulmao. Acessado em: 02/08/2019.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, pages 159–174.
Masood, A. et al. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images. Journal of Biomedical Informatics, 79:117 – 128.
Mishra, A. (2018). Metrics to evaluate your machine learning algorithm.
Disponível em: https://towardsdatascience.com/metrics-to-evaluate-your-machinelearning-algorithm-f10ba6e38234. Acessado em: 15/02/2020.
Safta, W. and Frigui, H. (2018). Multiple instance learning for benign vs. malignant classification of lung nodules in ct scans. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pages 490–494. IEEE.
Shaffie, Ahmed e Soliman, A. e. K. H. A. e. T. F. e. G. M. e. D. N. e. E. A. e. K. R. e. E.B. A. (2019). A novel ct-based descriptors for precise diagnosis of pulmonary nodules. In 2019 Conferencia Internacional da IEEE sobre Processamento de Imagens (ICIP) , pages 1400–1404.
Sharma, M., Bhatt, J. S., and Joshi, M. V. (2018). Early detection of lung cancer from ct images: nodule segmentation and classification using deep learning. In Tenth International Conference on Machine Vision (ICMV 2017), volume 10696, page 106960W. International Society for Optics and Photonics.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60. https://doi.org/10.1186/ s40537-019-0197-0.
Yan, X., Pang, J., Qi, H., Zhu, Y., Bai, C., Geng, X., Liu, M., Terzopoulos, D., and Ding, X. (2016). Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: A comparison between 2d and 3d strategies. In Asian Conference on Computer Vision, pages 91–101. Springer.
Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., and Qian, W. (2018). Agile convolutional neural network for pulmonary nodule classification using ct images. International journal of computer assisted radiology and surgery, 13(4):585–595.
Avelar, A. (2019). O que e auc e roc nos modelos de machine learning. Disponível em: https://medium.com/@eam.avelar/o-que-%C3%A9-auc-e-roc-nos-modelos-demachine-learning-2e2c4112033d. Accessed: 2020-02-15.
da Nobrega, R. V. M. et al. (2018). Lung nodule classification via deep transfer learning in ct lung images. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pages 244–249.
de Carvalho Filho, A. O., 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.
de Carvalho Filho, A. O., , et al. (2017). Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and svm. Medical & Biological Engineering & Computing, 55(8):1129–1146.
Deep, G., Kaur, L., and Gupta, S. (2016). Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval. Engineering Science and Technology, an International Journal, 19(4):1895 – 1909.
Dey, R., Lu, Z., and Hong, Y. (2018). Diagnostic classification of lung nodules using 3d neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 774–778.
Inca (2019). Instituto Nacional do Cancer - minist erio da saúde, cáncer de pulm ao. disponível em: https://www.inca.gov.br/tipos-de-cancer/cancer-de-pulmao. Acessado em: 02/08/2019.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, pages 159–174.
Masood, A. et al. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images. Journal of Biomedical Informatics, 79:117 – 128.
Mishra, A. (2018). Metrics to evaluate your machine learning algorithm.
Disponível em: https://towardsdatascience.com/metrics-to-evaluate-your-machinelearning-algorithm-f10ba6e38234. Acessado em: 15/02/2020.
Safta, W. and Frigui, H. (2018). Multiple instance learning for benign vs. malignant classification of lung nodules in ct scans. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pages 490–494. IEEE.
Shaffie, Ahmed e Soliman, A. e. K. H. A. e. T. F. e. G. M. e. D. N. e. E. A. e. K. R. e. E.B. A. (2019). A novel ct-based descriptors for precise diagnosis of pulmonary nodules. In 2019 Conferencia Internacional da IEEE sobre Processamento de Imagens (ICIP) , pages 1400–1404.
Sharma, M., Bhatt, J. S., and Joshi, M. V. (2018). Early detection of lung cancer from ct images: nodule segmentation and classification using deep learning. In Tenth International Conference on Machine Vision (ICMV 2017), volume 10696, page 106960W. International Society for Optics and Photonics.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60. https://doi.org/10.1186/ s40537-019-0197-0.
Yan, X., Pang, J., Qi, H., Zhu, Y., Bai, C., Geng, X., Liu, M., Terzopoulos, D., and Ding, X. (2016). Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: A comparison between 2d and 3d strategies. In Asian Conference on Computer Vision, pages 91–101. Springer.
Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., and Qian, W. (2018). Agile convolutional neural network for pulmonary nodule classification using ct images. International journal of computer assisted radiology and surgery, 13(4):585–595.
Publicado
15/09/2020
Como Citar
LIMA, Thiago; LUZ, Daniel; VERAS, Rodrigo ; ARAÚJO, Flavio.
Classificação de Nódulos Pulmonares Utilizando Redes Neurais Convolucionais 3D. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 120-130.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11507.
