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In this study, segmentation techniques are proposed based on; classic morphology and fuzzy morphology, and a comparison between them. The proposed methods were tested using the database of mini -MIAS, which contains 322 images. After the comparison the statistical results, it shows, the detection of tumor boundary with fuzzy morphology give the higher accuracy than the results in classic morphology. The accuracy is 60.69%, 58.61% respectively due to the high flexibility of foggy logic in dealing with the low lighting in the medical images.<\/jats:p>","DOI":"10.4018\/ijapuc.2019010103","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T14:25:32Z","timestamp":1552055132000},"page":"33-44","source":"Crossref","is-referenced-by-count":2,"title":["Breast Tumor Detection Via Fuzzy Morphological Operations"],"prefix":"10.4018","volume":"11","author":[{"given":"Mohammed Y.","family":"Kamil","sequence":"first","affiliation":[{"name":"Mustansiriyah University, Baghdad, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Mohammed","family":"Salih","sequence":"additional","affiliation":[{"name":"Mustansiriyah University, Baghdad, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJAPUC.2019010103-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.07.010"},{"key":"IJAPUC.2019010103-1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-016-4400-9"},{"key":"IJAPUC.2019010103-2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-013-0509-6"},{"key":"IJAPUC.2019010103-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.05.005"},{"key":"IJAPUC.2019010103-4","first-page":"75","author":"C. 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