{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:50:36Z","timestamp":1777704636200,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,8,4]],"date-time":"2018-08-04T00:00:00Z","timestamp":1533340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,12,24]]},"abstract":"<jats:p>\n                    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\n                    <jats:italic>d<\/jats:italic>\n                    \u200a=\u200a2, 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.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169850","type":"journal-article","created":{"date-parts":[[2018,8,5]],"date-time":"2018-08-05T06:38:28Z","timestamp":1533451108000},"page":"6101-6116","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning vector quantization inference classifier in breast abnormality classification"],"prefix":"10.1177","volume":"35","author":[{"given":"Chakkraphop","family":"Maisen","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand"},{"name":"Graduate School, Chiang Mai University, Chiang Mai, Thailand"}]},{"given":"Sansanee","family":"Auephanwiriyakul","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand"},{"name":"Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand"}]},{"given":"Nipon","family":"Theera-Umpon","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand"},{"name":"Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand"}]}],"member":"179","published-online":{"date-parts":[[2018,8,4]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"MuttarakM. 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