Jeleń, Łukasz ; Stankiewicz-Antosz, Izabela ; Chosia, Maria ; Jeleń, Michał
Współtwórca:Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Tytuł:Stochastic feature selection and machine learning for optimized cervical cancer classification
Tytuł publikacji grupowej: Temat i słowa kluczowe:cervical cancer classification ; machine learning ; feature selection ; convolutional neural networks ; stochastic models
Abstract: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. ; 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. ; 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.
Wydawca:Zielona Góra: Uniwersytet Zielonogórski
Data wydania: Typ zasobu: DOI: Strony: Źródło:AMCS, volume 35, number 4 (2025) ; kliknij tutaj, żeby przejść
Jezyk: Licencja CC BY 4.0: Prawa do dysponowania publikacją: