A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.

Cervical cancer is a significant health problem across the globe, especially in nations with low or middle incomes, where access to early screening is limited and contributes to high mortality rates. Traditional diagnostic methods, including Papanicolaou smears (Pap smears) and Human Papillomavirus testing (HPV testing), often suffer from accessibility constraints, lower accuracy, and scalability issues. Recent advancements in artificial intelligence (AI) and Machine Learning (ML) have exhibited significant efficacy by improving detection accuracy and streamlining the diagnostic procedure. This review discusses AI-driven methodologies, including deep learning models (EfficientNetB0, Cervical Net), hybrid frameworks, and ensemble techniques. This study examines important obstacles related to class imbalance, data privacy, and model interpretability while also presenting a solution known as the Adaptive Synthetic Minority Over-Sampling Technique combined with Tomek Links (Adaptive SMOTE Tomek) for data augmentation and federated learning (FL) for secure, distributed AI model training. The outcomes emphasize AI's vital role in improving cervical cancer screening and correspond with the World Health Organization's (WHO) 2030 goals for cervical cancer eradication. Future research should focus on real-world validation, improved generalization, and the integration of AI in clinical settings to ensure widespread usage and reliability. This work is useful for medical professionals in cervical cancer diagnosis, in addition to traditional screening methods.