Uma Análise Híbrida para Identificação de Câncer de Mama usando Sinais Térmicos
Resumo
Câncer de mama é o segundo tipo de câncer mais comum no mundo. Atualmente não existem métodos efetivos para prevenir esta doença. Porém, o diagnóstico e o tratamento em estágios iniciais aumentam as chances de cura. Depois de ser constatado que a temperatura de tecidos cancerosos é geralmente mais alta do que a de tecidos vizinhos saudáveis, a termografia passou a ser uma opção a ser considerada em estratégias para identificar esse tipo de câncer. Este artigo propõe um método híbrido de análise de Termografia Infravermelha Dinâmica com o objetivo de identificar pacientes com câncer de mama. Imagens desta modalidade de exame são processadas a fim de gerar sinais térmicos de ambas as mamas. Tais sinais são analisados por técnicas de aprendizagem de máquina supervisionada e não supervisionada. Na fase de testes, cinco modelos de classificação foram gerados, usando redes Bayesianas, redes neurais, tabelas de decisão, bagging e florestas randômicas. Os resultados dos testes mostraram que o método apresentado neste artigo é capaz de identificar pacientes com câncer de mama como também mostraram que redes Bayesianas é a melhor técnica de aprendizagem, pois apresentou acurácia de 100%. Além disso, foi obtida uma acurácia média de 93,63% entre todos os modelos de classificação testados.
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Silva, L. F., Olivera, G. O. S., Galvao, S., Silva, J. B., Santos, A. A. S. M. D., Muchaluat- Saade, D. C., and Conci, A. (2014a). Análise de séries temporais de sinais térmicos da mama para detecção de anomalias. WIM-XIV Workshop de Informática Médica-Anais CSBC, pages 1818–1827.
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Witten, I. H., Eibe, F., and Hall, M. A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, Burlington, MA 01803, USA, 3rd ed edition.
Bezerra, L., Oliveira, M., Rolim, T., Conci, A., Santos, F., Lyra, P., and Lima, R. (2013). Estimation of breast tumor thermal properties using infrared images. Signal Processing, 93(10):2851–2863.
Bolshakova, N. and Azuaje, F. (2003). Cluster validation techniques for genome expression data. Signal Processing, 83(4):825–833.
Bolshakova, N. and Azuaje, F. (2006). Estimating the number of clusters in dna microarray data. Methods of Information in Medicine, 45(2):153–157.
Borchartt, T. B., Conci, A., Lima, R. C. F., Resmini, R., and Sanchez, A. (2013). Breast thermography from an image processing viewpoint: A survey. Signal Processing, 93:2785–2803.
Borchartt, T. B., Resmini, R., Motta, L. S., Clua, E. W., Conci, A., Viana, M. J., Santos, L. C., Lima, R. C., and Sanchez, A. (2012). Combining approaches for early diagnosis of breast diseases using thermal imaging. International Journal of Innovative Computing and Applications, 4(3-4):163–183.
Cali´nski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1):1–27.
Chen, G., Jaradat, S. A., Banerjee, N., Tanaka, T. S., Ko, M. S. H., and Zhang, M. Q. (2002). Evaluation and comparison of clustering algorithms in anglying es cell gene expression data. Statistica Sinica, 12:241–262.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-1(2):224–227.
Flir (2015). Sc620 infrared camera system. http://www.flir.com/cs/apac/en/view/?id=41413. acessado em 20 de abril de 2013.
Gerasimova, E., Audit, B., Roux, S., Khalil, A., Gileva, O., Argoul, F., Naimark, O., and Arneodo, A. (2014). Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis. Frontiers in Physiology, 5(176).
Gerasimova, E., Audit, B., Roux, S. G., Khalil, A., Argoul, F., Naimark1, O., and Arneodo, A. (2013). Multifractal analysis of dynamic infrared imaging of breast cancer. EPL (Europhysics Letters), 104(6):68001–p1–68001–p6.
Gerasimova, E., Plekhov, O., Yu.Bayandin, O.Naimark, and G.Freynd (2012). Identification of breast cancer using analysis of thermal signals by nonlinear dynamics methods. International Conference on Quantitative InfraRed Thermography. , 11-14 June, Naples-Italy.
Goshtasby, A. A. (2005). 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. Wiley, New Jersey.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explorations, 11(1):10–18.
Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. PhD thesis, Department of Computer Science, The University of Waikato, Hamilton, New- Zealand.
Hall, M. A. and Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. on Knowl. and Data Eng., 15(6):1437–1447.
Han, J. and Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Burlington, MA 01803, USA, 2rd ed edition.
Hartigan, J. (1985). Statistical theory in clustering. Journal of Classification, 2(1):63–76.
Herman, C. (2013). The role of dynamic infrared imaging in melanoma diagnosis. Expert Review of Dermatology, 8(2):177–184.
Krzanowski,W. J. and Lai, Y. T. (1988). A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 44(1):23–34.
Lewis, J., Ackerman, M., and Sa, V. (2012). Human cluster evaluation and formal quality measures: A comparative study. Proc. 34th Conf. of the Cognitive Science Society CogSci.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. University of California Press, pages 281–297.
Myronenko, A. and Song, X. (2010). Intensity-based image registration by minimizing residual complexity. Medical Imaging, IEEE Transactions on, 29(11):1882–1891.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(0):53–65.
Silva, L. F., Marques, R. S., Carvalho, G. S., Santos, M. L. O., Fontes, C. A. P., Santos, A.
A. S. M. D., and Conci, A. (2013). Protocolo de captura de imagens térmicas da mama para construção de um banco público de exames. Encontro Nacional de Engenharia Biomecânica.
Silva, L. F., Olivera, G. O. S., Galvao, S., Silva, J. B., Santos, A. A. S. M. D., Muchaluat- Saade, D. C., and Conci, A. (2014a). Análise de séries temporais de sinais térmicos da mama para detecção de anomalias. WIM-XIV Workshop de Informática Médica-Anais CSBC, pages 1818–1827.
Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., Paiva, A. C., Bravo, R. S., and Conci, A. (2014b). A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics, 4(1):92–100.
Witten, I. H., Eibe, F., and Hall, M. A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, Burlington, MA 01803, USA, 3rd ed edition.
Publicado
20/07/2015
Como Citar
SILVA, Lincoln; OLIVEIRA, Giomar; BORCHARTT, Tiago; RESMINI, Roger; SANTOS, Alair; FONTES, Cristina; MUCHALUAT-SAADE, Débora; CONCI, Aura.
Uma Análise Híbrida para Identificação de Câncer de Mama usando Sinais Térmicos. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 15. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 71-80.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2015.10367.
