Endometrial Cancer Detection Using Pipeline Biopsies Through Machine Learning Techniques

Abstract

Endometrial carcinoma (EC) is a common uterine cancer that leads to morbidity and death linked to cancer. Advanced EC diagnosis exhibits a subpar treatment response and requires a lot of time and money. Data scientists and oncologists focused on computational biology due to its explosive expansion and computer-aided cancer surveillance systems. Machine learning offers prospects for drug discovery, early cancer diagnosis, and efficient treatment. It may be pertinent to use ML techniques in EC diagnosis, treatments, and prognosis. Analysis of ML utility in EC may spur research in EC and help oncologists, molecular biologists, biomedical engineers, and bioinformaticians advance collaborative research in EC. It also leads to customised treatment and the growing trend of using ML approaches in cancer prediction and monitoring. An overview of EC, its risk factors, and diagnostic techniques are covered in this study. It concludes a thorough investigation of the prospective ML modalities for patient screening, diagnosis, prognosis, and the deep learning models, which gave the good accuracy.