14 February 2015 Filter-based feature selection and support vector machine for false positive reduction in computer-aided mass detection in mammograms
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Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94451H (2015) https://doi.org/10.1117/12.2180524
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy
Abstract
In this paper, a method for reducing false positive in computer-aided mass detection in screening mammograms is proposed. A set of 32 features, including First Order Statistics (FOS) features, Gray-Level Occurrence Matrix (GLCM) features, Block Difference Inverse Probability (BDIP) features, and Block Variation of Local Correlation coefficients (BVLC) are extracted from detected Regions-Of-Interest (ROIs). An optimal subset of 8 features is selected from the full feature set by mean of a filter-based Sequential Backward Selection (SBS). Then, Support Vector Machine (SVM) is utilized to classify the ROIs into massive regions or normal regions. The method’s performance is evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC or AZ). On a dataset consisting about 2700 ROIs detected from mini-MIAS database of mammograms, the proposed method achieves AZ=0.938.
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V. D. Nguyen, V. D. Nguyen, D. T. Nguyen, D. T. Nguyen, T. D. Nguyen, T. D. Nguyen, V. A. Phan, V. A. Phan, Q. D. Truong, Q. D. Truong, } "Filter-based feature selection and support vector machine for false positive reduction in computer-aided mass detection in mammograms", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94451H (14 February 2015); doi: 10.1117/12.2180524; https://doi.org/10.1117/12.2180524
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