Local Binary Patterns Applied to Breast Cancer Classification in Mammographies


  • Eanes Torres Pereira Universidade Federal de Campina Grande
  • Sidney Pimentel Eleutério Universidade Estadual da Paraíba
  • João Marques Carvalho Universidade Federal de Campina Grande




Among all cancer types, breast cancer is the one with the second highest incidence rate for women. Mammography is the most used method for breast cancer detection, as it reveals abnormalities such as masses, calcifications, asymmetries and architectural distortions. In this paper, we propose a classification method for breast cancer that has been tested for six different cancer types: CALC, CIRC, SPIC, MISC, ARCH, ASYM. The proposed approach is composed of a SVM classifier trained with LBP features. The MIAS image database was used in the experiments and ROC curves were generated. To the best of our knowledge, our approach is the first to handle those six different cancer types using the same technique. One important result of the proposed approach is that it was tested over six different breast cancer types proving to be generic enough to obtain high classification results in all cases.


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Author Biographies

Eanes Torres Pereira, Universidade Federal de Campina Grande

Departamento de Sistemas e Computação

Sidney Pimentel Eleutério, Universidade Estadual da Paraíba

Departamento de Computação




How to Cite

Pereira, E. T., Eleutério, S. P., & Carvalho, J. M. (2014). Local Binary Patterns Applied to Breast Cancer Classification in Mammographies. Revista De Informática Teórica E Aplicada, 21(2), 32–46. https://doi.org/10.22456/2175-2745.46848



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