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<dc:title xml:lang="pl"><![CDATA[Stochastic feature selection and machine learning for optimized cervical cancer classification]]></dc:title>
<dc:creator><![CDATA[Jeleń, Łukasz]]></dc:creator>
<dc:creator><![CDATA[Stankiewicz-Antosz, Izabela]]></dc:creator>
<dc:creator><![CDATA[Chosia, Maria]]></dc:creator>
<dc:creator><![CDATA[Jeleń, Michał]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[cervical cancer classification]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[machine learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[feature selection]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[convolutional neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[stochastic models]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Liquid-based cytology (LBC) is a widely used diagnostic tool for cervical cancer diagnosis. However, the accuracy and efficiency of LBC-based cervical cancer classification are still limited due to the lack of standardized, scalable, and objective cytological assessment protocols. To address these gaps, this study develops and evaluates a machine learning framework that integrates various feature extraction techniques, feature selection methods, and machine learning classifiers to improve cervical cancer detection.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The results demonstrate that handcrafted and local binary pattern features achieve the best overall performance, with the SVM, gradient boosting and histogram-based gradient buffering reaching a 95.92% accuracy, highlighting the strength of combining morphological and texture descriptors to maximize their discriminative potential. Moreover, we provide a systematic comparison of different classification pipelines, offering insights into the feasibility of hybrid approaches, particularly in resource-constrained medical environments.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The promising results obtained in this study highlight the potential impact of machine learning in modern medical diagnostics, providing a clinically relevant, highly accurate, and efficient classification method for LBC slides.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2025]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/94236/AMCS_2025_35_4_5.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/105974/edition/94236/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:94236]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 35, number 4 (2025)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=138]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:105974]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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