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20 March 2014 Classification of breast lesions presenting as mass and non-mass lesions
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We aim to develop a CAD system for robust and reliable di erential diagnosis of breast lesions, in particular non-mass lesions. A necessary prerequisite for the development of a successful CAD system is the selection of the best subset of lesion descriptors. But an important methodological concern is whether the selected features are in uenced by the model employed rather than by the underlying characteristic distribution of descriptors for positive and negative cases. Another interesting question is how a particular classi er exploits the relationships between descriptors to increase the accuracy of the classi cation. In this work we set to: (1) Characterize kinetic, morphological and textural features among mass and non-mass lesions; (2) Examine feature spaces and compare selection of subset of features based on similarity of feature importance across feature rankings; (3) Compare two classi er performances namely binary Support Vector Machines (SVM) and Random Forest (RF) for the task of di erentiating between positive and negative cases when using binary classi cation for mass and non-mass lesions separately or when employing a multi-class classi cation. Breast MRI datasets consists of 243 (173 mass and 70 non-mass) lesions. Results show that RF variable importance used with RF-binary based classi cation optimized for mass and non-mass lesions separately o ers the best classi cation accuracy.
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Cristina Gallego-Ortiz and Anne L. Martel "Classification of breast lesions presenting as mass and non-mass lesions", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351Z (20 March 2014);

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