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Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17

C. H. Dagli
C. H. Dagli
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ASME Press
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This paper evaluated the contribution of both magnetic resonance mammography (MRM) image and non-image (NI) features (breast cancer risk factors) to benign ∕ malignant diagnostic accuracy using a new data set. This paper focus was using a new Kernealized-Partial Least Squares (K-PLS) and EP-SVM paradigms for this evaluation. An important initial finding is that (only) the non-imag , non-linear mapping region of the feature to diagnostic solution space contains significant diagnostic information, with a resultant overall Az of 0.77. The MRI image features of mass size, mass shape and mass margin yielded an Az of 0.96, which is excellent diagnostic performance. The EP-SVM Az diagnostic results for the same basic data set and statistical cross validation methods were slightly lower because “outliers” were included in these analyses.

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