Abstract
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.
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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: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_119
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DOI: https://doi.org/10.1007/978-3-540-73007-1_119
Publisher Name: Springer, Berlin, Heidelberg
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