Abstract
Breast cancer is the second cause of death of adult women in Mexico. Some of the risk factors for breast cancer that are visible in a mammography are the masses, calcifications, and the levels of mammographic density. While the first two have been studied extensively through the use of digital mammographies, this is not the case for the last one. In this paper, we address the automatic classification problem for the levels of mammographic density based on an evolutionary approach. Our solution comprises the following stages: thresholding, feature extractions, and the implementation of a genetic program. We performed experiments to compare the accuracy of our solution with other conventional classifiers. Experimental results show that our solution is very competitive and even outperforms the other classifiers in some cases.
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Acknowledgments
This work was supported by PRODEP under grant 511-6/17-8931 (ITCGUZ-CA-7), and by the TecNM under the projects 6055.17-P and 6307.17-P. Additionally, it was partially funded by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-66972-C5-4-R co-financed by FEDER funds.
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Fajardo-Delgado, D., Sánchez, M.G., Ochoa-Ornelas, R., Espinosa-Curiel, I.E., Vidal, V. (2018). Genetic Programming for the Classification of Levels of Mammographic Density. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_34
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