International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Endometrial cancer is one of the most common cancers affecting women worldwide. Early detection and accurate grading are crucial for improving survival rates, but traditional diagnostic methods can be invasive and unreliable. This work presents a deep learning approach that combines image preprocessing with Convolutional Neural Networks (CNN) for automated prediction and grading of endometrial cancer from histopathological images. Preprocessing steps, such as converting images to grayscale, filtering out noise, applying thresholds, sharpening images, and segmenting them, help to enhance image quality. The images are used to train CNN models. The study also compares these models with traditional machine learning classifiers like Super Vector Machine (SVM) and K-Nearest Neighbor (KNN). The model is evaluated using standard performance metrics, including accuracy, precision and recall. The proposed system shows promising results and demonstrates potential for integration into clinical workflows for early detection and support in decision-making

Keywords: Endometrial Cancer (EC), Histopathology, Image Preprocessing, CNN, Histopathological Images Machine Learning (ML), Deep Learning (DL).

Downloads: | DOI: 10.17148/IJARCCE.2025.141269