Application for breast cancer classification using Computational Intelligence techniques

• Manoel Oliveira UFC
• Felipe Muniz UFC
• Ruann Farrapo UFC

DOI: https://doi.org/10.5753/ercemapi.2020.11473

Resumo

In this work, a comparative study was carried out between two classification methods: The Multi layer Perceptron Artificial Neural Network (MLP ANN) and the method of classification of the Nearest Neighbors, used in the classification of the diagnosis of breast cancer. The data used in this work were taken from the UCI Machine Learning Repository and contains numerical data extracted from mammography images.In addition, the results were evaluated based on the cross-validation strategy.

Referências

Abdou, Y., Attwood, K., Cheng, T.-Y. D., Yão, S., Bandera, E. V., Zirpoli, G. R., Ondracek, R. P., Stein, L., Bshara, W., Khoury, T., et al. (2020). Racial differences in cd8+ t cell infiltration in breast tumors from black and white women. Breast Cancer Research, 22(1):1–10.

Athitsos, V., Alon, J., and Sclaroff, S. (2005). Efficient nearest neighbor classification using a cascade of approximate similarity measures. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 486–493. IEEE.

Athitsos, V. and Sclaroff, S. (2005). Boosting nearest neighbor classifiers for multiclass recognition. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops, pages 45–45. IEEE.

Dellani, P. R. and Borges, P. S. Implementação de um método para a classificação de microcalcificações pleomórficas invariante a posição, escala e orientação com rede neural de kohonen em mamografia convencional.

Dua, D. and Graff, C. (2017). UCI machine learning repository.

Guimar˜aes, A. M., Mathias, I. M., Dias, A. H., Ferrari, J. W., and Carlos, R. d. O. (2008). Módulo de validação cruzada para treinamento de redes neurais artificiais com algoritmos backpropagation e resilient propagation. Publicatio UEPG: Ciências Exatas e da Terra, Agrárias e Engenharias, 14(01).

Haddad, N. and SILVA, M. D. (2001). Mortalidade por neoplasmas em mulheres em idade reprodutiva-15 a 49 anos-no estado de s˜ão paulo, brasil, de 1991 a 1995. Revista da Associação Médica Brasileira, 47(3):221–230.

Haykin, S. (2001). Redes neurais: princípios e prática, 2a edição, tradução: Paulo martins engel. Editora: Bookman, Porto Alegre, Cap, 1(2):3.

Silva, I. d., Spatti, D. H., and Flauzino, R. A. (2010). Redes neurais artificiais para engenharia e ciências aplicadas. S˜ão Paulo: Artliber, 23(5):33–111.

Zeybek, Z., ç etinkaya, S., Hapoglu, H., and Alpbaz, M. (2006). Generalized delta rule (gdr) algorithm with generalized predictive control (gpc) for optimum temperature tracking of batch polymerization. Chemical engineering science, 61(20):6691–6700.