Stochastic feature selection and machine learning for optimized cervical cancer classification - Jeleń, Łukasz; Stankiewicz-Antosz, Izabela; Chosia, Maria; Jeleń, Michał (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: cervical cancer classification; machine learning; feature selection; convolutional neural networks; stochastic models