Descrição e Classificação de Nódulos Pulmonares em Imagens de Tomografia Computadorizada
Abstract
Lung cancer is the second most common in Brazil, early detection of solitary pulmonary nodules is essential for patient survival. This paper presents a computational methodology that aims to assist specialists in the detection and classification of pulmonary nodules. For the development of this methodology we performed tests with descriptors HOG, LBP, GLCM and Daisy, and the classifiers MLP, SVM and RF. The tests were performed on a segmented image set containing 1009 benign and 394 malignant nodules. The best results were achieved with the LBP descriptor and the SVM classifier, with an accuracy of 0.85, specificity of 0.84, sensitivity of 0.85, kappa of 0.70 and AUC of 0.85.
References
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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.
Published
2019-12-26
How to Cite
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: REGIONAL SCHOOL OF APPLIED COMPUTING FOR HEALTH (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 270-275.
