Abstract
Breast cancer is a global health problem principally affecting the female population. Digital mammograms are an effective way to detect this disease. One of the main indicators of malignancy in a mammogram is the presence of masses. However, their detection and diagnosis remains a difficult task. In this study, the impact of the combination of image descriptors and clinical data on the performance of conventional and kernel methods is presented. These models are trained with a dataset extracted from the public database BCDR-D01. The experimental results have shown that the incorporation of clinical data to image descriptors improves the performance of classifiers better than using the descriptors alone. Likewise, this combination, but using a nonlinear kernel function, improves the performance similar to those reported in the literature for this dataset.
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Hernández-Hernández, S., Orantes-Molina, A., Cruz-Barbosa, R. (2018). Improving Breast Mass Classification Through Kernel Methods and the Fusion of Clinical Data and Image Descriptors. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_26
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