Descrição e Classificação de Nódulos Pulmonares em Imagens de Tomografia Computadorizada
Resumo
O câncer de pulmão é o segundo mais comum no Brasil, a detecção precoce de nódulos pulmonares solitário é essencial para a sobrevivência do paciente. Este trabalho apresenta uma metodologia computacional que visa auxiliar especialistas na detecção e classificação de nódulos pulmonares. Para o desenvolvimento dessa metodologia realizamos testes com descritores HOG, LBP, GLCM e Daisy, e os classificadores MLP, SVM e RF. Os testes foram realizados em um conjunto de imagens segmentadas contendo 1009 nódulos benignos e 394 malignos. Os melhores resultados foram alcançados com o descritor LBP e o classificador SVM, com uma acurácia de 0,85, especificidade de 0,84, sensibilidade de 0,85, kappa de 0,70 e AUC de 0,85.
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.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1.
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.
Inca (2019). Instituto Nacional do Câncer - ministério da saúde, câncer de pulmão. https://www.inca.gov.br/tipos-de-cancer/cancer-de-pulmao. Acessado em: 02/08/2019.
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.
Nicolas, P. R. (2015). Scala for machine learning. Packt Publishing Ltd.
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.
Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Integr. Ser. Inf. Syst, 36:1–12.
Tang, H., Kim, D. R., and Xie, X. (2018). Automated pulmonary nodule detection using 3d deep convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 523–526.
Tola, E., Lepetit, V., and Fua, P. (2010). Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):815–830.
Awad, A. I. and Hassaballah, M. (2016). Image feature detectors and descriptors. Studies in Computational Intelligence. Springer International Publishing, Cham.
Bell, J. (2014). Machine learning: hands-on for developers and technical professionals. John Wiley & Sons.
Cuzzocrea, A., Manco, G., and Masciari, E. (2012). Effective detection of xml outliers. In KES, pages 1221–1232.
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.
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1.
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.
Inca (2019). Instituto Nacional do Câncer - ministério da saúde, câncer de pulmão. https://www.inca.gov.br/tipos-de-cancer/cancer-de-pulmao. Acessado em: 02/08/2019.
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.
Nicolas, P. R. (2015). Scala for machine learning. Packt Publishing Ltd.
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.
Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Integr. Ser. Inf. Syst, 36:1–12.
Tang, H., Kim, D. R., and Xie, X. (2018). Automated pulmonary nodule detection using 3d deep convolutional neural networks. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 523–526.
Tola, E., Lepetit, V., and Fua, P. (2010). Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):815–830.
Publicado
26/12/2019
Como Citar
LIMA, T. J. B.; ARAÚJO, F. H. D.; VIEIRA, P. A.; CARVALHO, N. R. de S.; RODRIGUES, L. A..
Descrição e Classificação de Nódulos Pulmonares em Imagens de Tomografia Computadorizada. In: ESCOLA REGIONAL DE COMPUTAÇÃO APLICADA À SAÚDE (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 270-275.
