Abstract
One of the best prevention measures against breast cancer is the early detection of calcifications through mammograms. Detecting calcifications in mammograms is a difficult task because of their size and the high content of similar patterns in the image. This brings the necessity of creating automatic tools to find whether a mammogram presents calcifications or not. In this paper we introduce the combination of machine vision and data-mining techniques to detect calcifications (including micro-calcifications) in mammograms that achieves an accuracy of 92.6 % with decision trees and 94.3 % with a back-propagation neural network. We also focus in the data-mining task with decision trees to generate descriptive patterns based on a set of characteristics selected by our domain expert. We found that these patterns can be used to support the radiologist to confirm his diagnosis or to detect micro-calcifications that he could not see because of their reduced size.
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Flores, B.A., Gonzalez, J.A. (2004). Data Mining with Decision Trees and Neural Networks for Calcification Detection in Mammograms. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_24
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DOI: https://doi.org/10.1007/978-3-540-24694-7_24
Publisher Name: Springer, Berlin, Heidelberg
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