Abstract: One of the most common gynaecological cancers affecting women globally is endometrial cancer, which develops from the lining of the uterus. The prognosis is improved by early diagnosis, but traditional techniques like biopsy and ultrasound are frequently intrusive, costly, or inaccessible. Recent developments in artificial intelligence, specifically in the areas of machine learning (ML) and image processing, present promising instruments for the automated, non-invasive, and precise detection and grading of endometrial cancer. This survey investigates how to improve the quality of histopathological images for analysis using image preprocessing methods like RGB to grayscale conversion, noise reduction, thresholding, segmentation, and feature extraction. Additionally, it assesses how well deep learning models—particularly Convolutional Neural Networks (CNNs) and transfer learning techniques—classify malignant tissues and forecast tumour stage. and
Keywords: Endometrial Cancer, Image Preprocessing, Machine Learning, CNN, Histopathology, Medical Imaging, Transfer Learning, Deep Learning.
|
DOI:
10.17148/IJARCCE.2025.14650
