Advanced Methodologies in Cervical Cancer Classification using Deep Learning and Ensemble Techniques

Cervical cancer remains a major cause of morbidity among women globally, particularly in regions with limited healthcare infrastructure. Despite advancements in screening techniques, manual cytological interpretation of Pap smear images is subject to observer bias and diagnostic inconsistency. This paper introduces a hybrid deep learning framework integrating InceptionV3, MobileNetV2, and Inception ResNetV2, fused with a fuzzy-distance ensemble method to improve reliability and accuracy in cervical cancer cell classification. Our approach builds upon existing works in deep cytology analysis and addresses limitations in generalization and class imbalance found in prior research. The system demonstrates superior performance on the SiPaKMeD dataset, showing strong potential for real-world clinical deployment.