Abstract
This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier’s output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, P., Kumar, K., Verma, B. (2005). A Hybrid Classifier for Mass Classification with Different Kinds of Features in Mammography. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_38
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DOI: https://doi.org/10.1007/11540007_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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