This work evaluates the efficiency of Independent Component Analysis in conjunction with neural network classifiers to detect microcalcification clusters in digitized mammograms, the most important non invasive sign of breast cancer. The widespread Digital Database for Screening Mammography was used as the source for digitized mammograms. The results seem to suggest that this technique is suitable to deal with the noisy mammogram environment. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Gallardo-Caballero, R., García-Orellana, C. J., González-Velasco, H. M., & Macías-Macías, M. (2007). Independent component analysis applied to detection of early breast cancer signs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 988–998). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_119
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