1. Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.
- Author
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Huiya Wang, Jun Feng, Hongyu Wang, Wang, Huiya, Feng, Jun, and Wang, Hongyu
- Subjects
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CALCIFICATIONS of the breast , *MAMMOGRAMS , *SUPPORT vector machines , *ALGORITHMS , *ARTIFICIAL neural networks - Abstract
Background: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer.Objective: To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required.Methods: In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues.Results: From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively.Conclusion: The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate. [ABSTRACT FROM AUTHOR]- Published
- 2017
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