A novelty detection approach to classification of breast tissue containing microcalcifications
[ X ]
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computing Machinery, Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Appearance 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.
Açıklama
ACM Special Interest Group on Applied Computing (ACM SIGAPP); Association of Convergent Computing Technology (ACCT); Jagiellonian University in Krakow; Korean Institute of Smart Media
2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017 -- 20 September 2017 through 23 September 2017 -- Krakow -- 131462
2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017 -- 20 September 2017 through 23 September 2017 -- Krakow -- 131462
Anahtar Kelimeler
Breast cancer, Microcalcification, One-class support vector machines, Principal component analysis
Kaynak
Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017
WoS Q Değeri
Scopus Q Değeri
Cilt
2017-January
