Avsar, ErcanBulus, Kurtulus2025-01-062025-01-062017978-145035027-310.1145/3129676.31296802-s2.0-85043350958https://doi.org/10.1145/3129676.3129680https://hdl.handle.net/20.500.14669/1306ACM Special Interest Group on Applied Computing (ACM SIGAPP); Association of Convergent Computing Technology (ACCT); Jagiellonian University in Krakow; Korean Institute of Smart Media2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017 -- 20 September 2017 through 23 September 2017 -- Krakow -- 131462Appearance of microcalcifications in mammograms is one of the early signs of breast cancer. In this work, one-class support vector machines (SVM), a novelty detection method, is utilized for detection of the mammogram samples containing microcalcifications. These samples are small regions of the mammograms with the size of 25x25 pixels. Each of the samples are represented by 25 features that are already proven to be accurate identifiers of the microcalcifications. Since the obtained classification performance of one-class SVM with all these 25 features is very low (accuracy = 0.5575, sensitivity = 0.2107, specificity = 0.9042), number of these features is reduced by using principal component analysis (PCA). Training a classifier only with the PCA features achieves an improved performance (accuracy = 0.9464, sensitivity = 1.0000, specificity = 0.8927) where the number of false negative samples is reduced from 206 to 0. © 2017 Association for Computing Machinery.eninfo:eu-repo/semantics/closedAccessBreast cancerMicrocalcificationOne-class support vector machinesPrincipal component analysisConference Object1031002017-January