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<dc:title xml:lang="pl"><![CDATA[Enhancing multi-class prediction of skin lesions with feature importance assessment]]></dc:title>
<dc:creator><![CDATA[Paulauskaite-Taraseviciene, Agne]]></dc:creator>
<dc:creator><![CDATA[Sutiene, Kristina]]></dc:creator>
<dc:creator><![CDATA[Dimsa, Nojus]]></dc:creator>
<dc:creator><![CDATA[Valiukeviciene, Skaidra]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[skin lesion]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[feature extraction]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[graph theory]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[multi-class prediction]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[SHAP values]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Numerous image processing techniques have been developed for the identification of various types of skin lesions. In real-world scenarios, the specific lesion type is often unknown in advance, leading to a multi-class prediction challenge. The available evidence underscores the importance of employing a comprehensive array of diverse features and subsequently identifying the most important ones as a crucial step in visual diagnostics. For this purpose, we addressed both binary and five-class classification tasks using a small dataset, with skin lesions prevalent in Lithuania. The model was trained using a rich set of 662 features, encompassing both conventional image features and graph-based ones, which were obtained from the superpixel graph generated using Delaunay triangulation. We explored the influence of feature importance determined by SHAP values, resulting in a weighted F1-score of 92.48% for the two-class classification and 71.21% for the five-class prediction.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Woźniak, Marcin - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kumar, Yogesh - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Ijaz, Muhammad Fazal - ed.]]></dc:contributor>
<dc:date><![CDATA[2024]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/87179/AMCS_2024_34_4_6.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101891/edition/87179/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:87179]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 34, number 4 (2024)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=134]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101891]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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