Predição do Câncer de Mama com Aplicação de Modelos de Inteligência Computacional
DOI:
https://doi.org/10.5540/tema.2019.020.02.229Keywords:
Rede Neural, Máquina de Vetor de Suporte, Câncer de Mama.Abstract
O uso de modelos para diagnóstico auxiliado por computador (CAD) tem sido proposto para auxiliar na detecção e classificação do câncer de mama. Neste trabalho, avaliou-se o desempenho dos modelos de rede neural de perceptrons de múltiplas camadas e máquina de vetores de suporte não linear, para classificar nódulos de câncer de mama. Dez características morfológicas, do contorno de 569 amostras, foram usadas como entrada nos classificadores. Os melhores resultados obtidos para a acurácia e taxa de falso negativo no modelo de máquina de vetor de suporte não linear foram 98,58% e 1,96%, respectivamente. O modelo de rede neural apresentou desempenho inferior ao classificador de máquina de vetor de suporte não linear. Os resultados médios obtidos, com a aplicação dos modelos propostos, mostram-se promissores, na classificação do câncer de mama.References
W. H. Organization, Cancer Report. International Agency for Research on Cancer. Geneva, Switzerland, who library ed., 2014.
INCA, Estimativas 2018. Incidência de Câncer no Brasil. Instituto Nacional do Câncer, 2018.
M. Mavroforakis, H. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, "Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines",European Journal of Radiology, vol. 54, no. 1, pp. 80-89, 2005.
R. Kala, R. Janghel, R. Tiwari, and A. Shukla, "Diagnosis of breast cancer by modular evolutionary neural networks", International Journal of Biomedical Engineering and Technology, vol. 7, no. 2, pp. 194-211, 2011.
I. Saritas, "Prediction of breast cancer using artificial neural networks",Journal of Medical Systems, vol. 36, no. 5, pp. 2901-2907, 2012.
S. Agrawal and J. Agrawal, "Neural network techniques for cancer prediction: A survey", Procedia Computer Science, vol. 60, pp. 769-774, 2015.
M. Pérez, M. E. Benalcázar, E. Tusa, W. Rivas, and A. Conci, "Mammogram classification using back-propagation neural networks and texture feature descriptors", in Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE, pp. 1- 6, IEEE, 2017.
K. Menaka and S. Karpagavalli, "Breast cancer classfication using support vector machine and genetic programming", International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 7, 2013.
H. Asri, H. Mousannif, H. Al Moatassime, and T. Noel, "Using machine learning algorithms for breast cancer risk prediction and diagnosis", Procedia Computer Science, vol. 83, pp. 1064-1069, 2016.
H. Jiang, W.-K. Ching, W.-S. Cheung, W. Hou, and H. Yin, "Hadamard kernel svm with applications for breast cancer outcome predictions", BMC systems biology, vol. 11, no. 7, p. 138, 2017.
M.-W. Huang, C.-W. Chen, W.-C. Lin, S.-W. Ke, and C.-F. Tsai, "Svm and svm ensembles in breast cancer prediction", PloS one, vol. 12, no. 1, 2017.
S. Haykin, Redes neurais: princípios e prática 2 ed. Bookman Editora, 2001.
A. de Pádua Braga, A. C. P. de Leon Ferreira, and T. B. Ludermir, Redes neurais artificiais: teoria e aplicações. LTC Editora, 2012.
A. J. Smola and B. Schölkopf, "A tutorial on support vector regression", Statistics and computing, vol. 14, no. 3, pp. 199-222, 2004.
V. Vapnik, Statistical learning theory. 1998. Wiley, New York, 1998.
A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis, "kernlab-an s4 package for kernel methods in r", Journal of statistical software, vol. 11, no. 9, pp. 1-20, 2014.
S. Fritsch, F. Guenther, and M. F. Guenther, "Package neuralnet", The Comprehensive R Archive Network, 2016.
S. Shanthi and V. M. Bhaskaran, "A novel approach for detecting and classifying breast cancer in mammogram images", International Journal of Intelligent Information Technologies (IJIIT), vol. 9, no. 1, pp. 21-39, 2013.
Downloads
Published
How to Cite
Issue
Section
License
Copyright
Authors of articles published in the journal Trends in Computational and Applied Mathematics retain the copyright of their work. The journal uses Creative Commons Attribution (CC-BY) in published articles. The authors grant the TCAM journal the right to first publish the article.
Intellectual Property and Terms of Use
The content of the articles is the exclusive responsibility of the authors. The journal uses Creative Commons Attribution (CC-BY) in published articles. This license allows published articles to be reused without permission for any purpose as long as the original work is correctly cited.
The journal encourages Authors to self-archive their accepted manuscripts, publishing them on personal blogs, institutional repositories, and social media, as long as the full citation is included in the journal's website version.
