Abstract
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. We study the application of these algorithms as a classification system in order to differentiate benign from malignant mass, obtained from MIAS database, and we compare the classifier with other classification techniques to test the accuracy.
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© 2011 Springer-Verlag Berlin Heidelberg
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Raman, V., Then, P., Sumari, P. (2011). Mammogram Problem Solving Approach: Building CBR Classifier for Classification of Masses in Mammogram. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_49
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DOI: https://doi.org/10.1007/978-3-642-19542-6_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19541-9
Online ISBN: 978-3-642-19542-6
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