Learning vector quantization inference classifier in breast abnormality classification

Issue title: Special Section: Green and Human Information Technology

Guest editors: Seong Oun Hwang

Article type: Research Article

Authors: Maisen, Chakkraphop^{a; b} | Auephanwiriyakul, Sansanee^{a; d; *} | Theera-Umpon, Nipon^{c; d}

Affiliations: [a] Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand | [b] Graduate School, Chiang Mai University, Chiang Mai, Thailand | [c] Electrical Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand | [d] Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand

Correspondence: [*] Corresponding author. Sansanee Auephanwiriyakul, E-mail: [email protected].

Abstract: The Mammographic image is a tool for observing breast cancer. Analyzing difficulties include shape, size variety, nearby tissue, and noise. In this paper, we propose a method to classify mammogram abnormalities based on learning vector quantization inference classifier (LVQIC) with fuzzy co-occurrence matrix (FCOM) textural features. The system is implemented on the Mini-MIAS data set with a 5-class problem, i.e., the classification of architectural distortion (AD), spiculated mass (SPIC), calcification (CALC), well-defined/circumscribed masses (CIRC), and normal (NORM). The implementation is also on a 2-class problem consisting of AD-vs-All, SPIC-vs-All, CALC-vs-All, CIRC-vs-All, and NORM/abnormal. The best blind test result is from the 5-class problem with features from fuzzy co-occurrence matrix (FCOM) with 4 clusters, co-occurrence distance d = 2, and 16 prototypes per class. The best classification result is 100% correct classification with 0.03, 0.04, 0.06, and 0.02 false positive rate for AD, SPIC, CALC, and CIRC, respectively.

Keywords: Mammograms, breast cancer, breast abnormality detection, neuro fuzzy, feature selection

DOI: 10.3233/JIFS-169850

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6101-6116, 2018