Learning vector quantization inference classifier in breast abnormality classification
Chakkraphop Maisen, Sansanee Auephanwiriyakul, Nipon Theera‐Umpon · Journal of Intelligent & Fuzzy Systems · 2018
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.