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.