An in-depth exploration of CNN-based deep learning models in cervical carcinoma analysis

Cervical cancer has an extreme effect on women's health worldwide, recognized as the 4th most significant contributor to cancer fatalities among female. World Health Organization (WHO) states that there was on 660,000 new reports and 350,000 death occurred. Detecting the disease early can lead to a significant decrease in the death rate up to 80 %. Currently, doctors diagnose cervical cancer by examining cervical biopsies through Pap smears and colposcopy images. However this techniques is time-intensive, taking up to several hours per case and susceptible to misdiagnosis and diagnostic error between 10 and 30 %.Deep learning has illustrated significant potential for addressing biomedical challenges such as analysis of medical images, disease forecasting, and image partitioning. AI-powered diagnostic methods utilizing deep learning models–such as CNNs, DenseNets, and U-Nets—have achieved classification accuracies exceeding 95 % on datasets like Herlev and SIPaKMeD. This paper surveys diverse deep learning strategies that were implemented for the identification and analysis of cervical carcinoma, emphasizing their performance metrics, datasets and clinical applicability.