<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-P9NI3QOE</identifier><date>2023</date><creator>Muslim Jasim, Hend</creator><relation>documents/doc/P/URN_NBN_SI_doc-P9NI3QOE_001.pdf</relation><relation>documents/doc/P/URN_NBN_SI_doc-P9NI3QOE_001.txt</relation><format format_type="volume">47</format><format format_type="issue">8</format><format format_type="type">article</format><format format_type="extent">str. 77-87</format><identifier identifier_type="DOI">10.31449/inf.v47i8.4840</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID">208718595</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-P9NI3QOE</identifier><language>eng</language><publisher publisher_location="Ljubljana">Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">MRI slike</subject><subject language_type_id="slv">odkrivanje raka</subject><subject language_type_id="slv">rak (medicina)</subject><subject language_type_id="slv">segmentacija slik</subject><title>Provably efficient multi-cancer image segmentation based on multi-class fuzzy entropy</title></Record>