Detecção de Nódulos da Tireoide em Exames de Termografia utilizando Redes Neurais Convolucionais em Cascata
Resumo
Os exames de termografia podem ser utilizados como um meio não invasivo para a detecção de doenças através da análise de imagens térmicas do corpo humano. O presente trabalho apresenta um método para detectar nódulos da glândula tireoide em imagens térmicas utilizando duas Redes Neurais Convolucionais (CNN's) em cascata. A primeira CNN atua na geração de candidatos a nódulo, e a segunda faz o refinamento da detecção, eliminando falsos positivos. Os experimentos foram realizados com imagens térmicas de 20 pacientes. O resultado final atinge 97% de acurácia, 95% de sensibilidade e 80% de f1-score utilizando duas CNN's AlexNet.
Referências
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Moran, M. B., Conci, A., González, J. R., Araújo, A. S., Fiirst, W., Damião, C. P., Lima, G. A., and da Cruz Filho, R. A. (2018). Identification of thyroid nodules in infrared images by convolutional neural networks. In 2018 International joint conference on neural networks (IJCNN), pages 1–7. IEEE.
Nguyen, Q. T., Lee, E. J., Huang, M. G., Park, Y. I., Khullar, A., and Plodkowski, R. A. (2015). Diagnosis and treatment of patients with thyroid cancer. American health & drug benefits, 8(1):30.
Rahib, L., Smith, B. D., Aizenberg, R., Rosenzweig, A. B., Fleshman, J. M., and Matrisian, L. M. (2014). Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the united states. Cancer research, 74(11):2913–2921.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
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Brioschi, M., Teixeira, M., Silva, F., and Colman, D. (2010). Medical thermography textbook: principles and applications. Editora e Livrarai Andreoli.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639):115–118.
Fiirst, W., Montenegro, A., Machenry, T., Li, H., Du, C., and Conci, A. (2018). Introducing tensor analysis for biomedical image registration. 6o. Encontro Nacional de Engenharia Biomecanica (ENEBI 2018), Aguas de Lindoia, SP (proc. ABCM, SBB).
Fiirst, W. G., Resmini, R., and Conci, A. (2019). Desenvolvimento e comparação de métodos para geração de séries temporais para imagens térmicas da tireoide. In Anais Estendidos do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 43– 48. SBC.
Gonzalez, J. (2017). Um estudo sobre a possibilidade do uso de imagens infravermelhas na analise de nodulos de tireoide. Dissertation, IC, Universidade Federal Fluminense.
González, J., Conci, A., Moran, M., Araujo, A., Paes, A., Damião, C., and Fiirst, W. (2019). Analysis of static and dynamic infrared images for thyroid nodules investigation. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pages 1–7. IEEE.
Guizar-Sicairos, M., Thurman, S., and Fienup, J. (2008). Efficient subpixel image registration algorithms. Optics letters, 33 2:156–8.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Hernandez Moran, M., Conci, A., and Araújo, A. (2019). Evaluation of quantitative features and convolutional neural networks for nodule identification in thyroid thermographies. pages 747–751.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25:1097–1105.
Lévy, D. and Jain, A. (2016). Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2018). Focal loss for dense object detection.
McPhail, S., Johnson, S., Greenberg, D., Peake, M., and Rous, B. (2015). Stage at diagnosis and early mortality from cancer in england. British journal of cancer, 112(1):S108– S115.
Moran, M. B., Conci, A., González, J. R., Araújo, A. S., Fiirst, W., Damião, C. P., Lima, G. A., and da Cruz Filho, R. A. (2018). Identification of thyroid nodules in infrared images by convolutional neural networks. In 2018 International joint conference on neural networks (IJCNN), pages 1–7. IEEE.
Nguyen, Q. T., Lee, E. J., Huang, M. G., Park, Y. I., Khullar, A., and Plodkowski, R. A. (2015). Diagnosis and treatment of patients with thyroid cancer. American health & drug benefits, 8(1):30.
Rahib, L., Smith, B. D., Aizenberg, R., Rosenzweig, A. B., Fleshman, J. M., and Matrisian, L. M. (2014). Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the united states. Cancer research, 74(11):2913–2921.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946.
Zhu, R., Zhang, R., and Xue, D. (2015). Lesion detection of endoscopy images based on convolutional neural network features. In 2015 8th International Congress on Image and Signal Processing (CISP), pages 372–376. IEEE.
Publicado
15/06/2021
Como Citar
ANCHIETA JÚNIOR, Ricardo José Fernandes; SILVA, Italo Francyles Santos da; SILVA, Aristófanes Corrêa; PAIVA, Anselmo Cardoso de.
Detecção de Nódulos da Tireoide em Exames de Termografia utilizando Redes Neurais Convolucionais em Cascata. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 269-280.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2021.16071.
