A Comparative Analysis of Artificial Intelligence based models for the Identification of Uterine Cancer

Uterine cancer identification is a critical task in medical diagnostics since early detection improves patient health. Machine learning (ML) and deep learning (DL) have both showed promise in this discipline as artificial intelligence has advanced. The present paper offers a thorough analysis and comparison of ML and DL methods for uterine cancer detection. Several algorithms, datasets, and assessment criteria have been evaluated in this study in order to ascertain which method performs best. Graphical presentations and tabular comparisons are used to examine the data and show the advantages and disadvantages of each approach. The results conclude that DL based models perform better than typical ML models in terms of accuracy and predictive power, even if both strategies offer advantages.