{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:38:39Z","timestamp":1770277119794,"version":"3.49.0"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (    95.00  \u00b1  0.57    %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A     z     value equal to     0.97  \u00b1  0.01    .<\/jats:p>","DOI":"10.3390\/jimaging5090076","type":"journal-article","created":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T10:56:06Z","timestamp":1568285766000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6488-8473","authenticated-orcid":false,"given":"Nashid","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-862X","authenticated-orcid":false,"given":"Erika","family":"R. E. Denton","sequence":"additional","affiliation":[{"name":"Norfolk and Norwich University Hospital, Norwich NR4 7UY, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4360-0896","authenticated-orcid":false,"given":"Reyer","family":"Zwiggelaar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., and Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. 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