Revisão sistemática da literatura sobre a utilização de Convolutional Neural Networks para detecção de nódulos pulmonares em imagens de tomografia computadorizada
Resumo
O câncer de pulmão é uma das principais causas de mortalidade no mundo, como apontado pela Agência Internacional de Pesquisa em Câncer (IARC). A Tomografia Computadorizada é amplamente utilizada para detecção de nódulos pulmonares, mas a análise manual dessas imagens é desafiadora devido à complexidade e ao grande volume de dados. As CNNs têm sido empregadas para aprimorar a detecção automática de nódulos, demonstrando grande eficácia na identificação de padrões complexos em imagens médicas. Este estudo realiza uma Revisão Sistemática da Literatura a fim de entender as tendências no uso de CNNs na detecção de nódulos pulmonares, analisando essas arquiteturas e bases de dados.Referências
Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, 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.
Chen, Y. and Xie, M. (2024). Improved focus on hard samples for lung nodule detection. In Proceedings of the 2024 6th International Conference on Control and Computer Vision, pages 21–26.
Dolejšı, M. (2007). Detection of pulmonary nodules from ct scans. PhD thesis, Citeseer.
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.
Guimarães, N. S., Ferreira, A. J., Silva, R. d. C. R., de Paula, A. A., Lisboa, C. S., Magno, L., Ichiara, M. Y., and Barreto, M. L. (2022). Deduplicating records in systematic reviews: there are free, accurate automated ways to do so. Journal of Clinical Epidemiology, 152:110–115.
Jiang, H., Ma, H., Qian, W., Gao, 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, 22:1227–1237.
Johnson, N. and Phillips, M. (2018). Rayyan for systematic reviews. Journal of Electronic Resources Librarianship, 30(1):46–48.
Kamiya, H., Murayama, S., Kakinohana, Y., and Miyara, T. (2011). Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional ct images. Clinical Imaging, 35(2):123–126.
Kirby, J. S., Armato, S. G., Drukker, K., Li, F., Hadjiiski, L., Tourassi, G. D., Clarke, L. P., Engelmann, R. M., Giger, M. L., Redmond, G., and Farahani, K. (2016). LUNGx Challenge for computerized lung nodule classification. Journal of Medical Imaging, 3(4):044506.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12):6999–7019.
McKeown, S. and Mir, Z. M. (2021). Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references. Systematic Reviews, 10(1):38.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão Sistemática da Literatura em Engenharia de Software: teoria e prática. Elsevier.
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., et al. (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.
Silva, A. C., Carvalho, P. C. P., and Gattass, M. (2005). Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images. Computer Methods and Programs in Biomedicine, 79(1):31–38.
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 Recognit., 85:109–119.
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 Computer Vision – ACCV 2016 Workshops. Springer International Publishing.
Chen, Y. and Xie, M. (2024). Improved focus on hard samples for lung nodule detection. In Proceedings of the 2024 6th International Conference on Control and Computer Vision, pages 21–26.
Dolejšı, M. (2007). Detection of pulmonary nodules from ct scans. PhD thesis, Citeseer.
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.
Guimarães, N. S., Ferreira, A. J., Silva, R. d. C. R., de Paula, A. A., Lisboa, C. S., Magno, L., Ichiara, M. Y., and Barreto, M. L. (2022). Deduplicating records in systematic reviews: there are free, accurate automated ways to do so. Journal of Clinical Epidemiology, 152:110–115.
Jiang, H., Ma, H., Qian, W., Gao, 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, 22:1227–1237.
Johnson, N. and Phillips, M. (2018). Rayyan for systematic reviews. Journal of Electronic Resources Librarianship, 30(1):46–48.
Kamiya, H., Murayama, S., Kakinohana, Y., and Miyara, T. (2011). Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional ct images. Clinical Imaging, 35(2):123–126.
Kirby, J. S., Armato, S. G., Drukker, K., Li, F., Hadjiiski, L., Tourassi, G. D., Clarke, L. P., Engelmann, R. M., Giger, M. L., Redmond, G., and Farahani, K. (2016). LUNGx Challenge for computerized lung nodule classification. Journal of Medical Imaging, 3(4):044506.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12):6999–7019.
McKeown, S. and Mir, Z. M. (2021). Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references. Systematic Reviews, 10(1):38.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão Sistemática da Literatura em Engenharia de Software: teoria e prática. Elsevier.
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., et al. (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.
Silva, A. C., Carvalho, P. C. P., and Gattass, M. (2005). Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images. Computer Methods and Programs in Biomedicine, 79(1):31–38.
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 Recognit., 85:109–119.
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 Computer Vision – ACCV 2016 Workshops. Springer International Publishing.
Publicado
28/05/2025
Como Citar
SILVA, Joyce Moura.
Revisão sistemática da literatura sobre a utilização de Convolutional Neural Networks para detecção de nódulos pulmonares em imagens de tomografia computadorizada. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 17. , 2025, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 109-118.
DOI: https://doi.org/10.5753/enucompi.2025.9732.
