Machine Learning-Based Approach for Uterine Cancer Detection and Classifier Evaluation - Samarasam, Brindha; Justin, Judith (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2025)
The necessity of early diagnosis of abnormal cell growth is critical to support patient monitoring and earlier clinical analysis. Uterine cancer is the most common gynecological malignancy among women, with endometrial cancer being the predominant type occurring in the endometrial layer. Endometrial cancer is a commonly identified type of uterine cancer that majorly occurs in the endometrial layer. This research applies machine learning (ML) algorithms to detect uterine cancer using texture-based features extracted from medical images. Specifically, a hybrid combination of Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) properties is proposed to derive 34 features, including entropy, long-run emphasis, short-run low grey level emphasis, and high grey level run emphasis. To ensure data quality, a comprehensive dataset was collected and preprocessed, followed by the implementation of an improved approach for feature normalization and ranking. The top-ranked features were then used to train and validate multiple ML algorithms, including Adaptive Neuro-Fuzzy Inference System (ANFIS), K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Radial Basis Function (RBF), Support Vector Machine (SVM), Naïve Bayes (NB), and Artificial Neural Network (ANN). Results show that the best-performing algorithm achieves an accuracy of 97.3%, sensitivity of 96.3%, and specificity of 99.2%. The algorithm's performance was further validated using Receiver Operating Characteristics (ROC) analysis and F1 scores, both of which demonstrated superior predictive capability. Additionally, Explainable AI (XAI) techniques were integrated to elucidate the features and patterns recognized by the algorithm as indicative of endometrial carcinoma. Layer-wise relevance propagation (LRP) was employed to backtrack the neural network’s output decisions to the input features, highlighting the most influential factors in the algorithm's predictions. This research demonstrates the potential of applying ML algorithms to improve early detection of uterine cancer, offering a non-invasive, accurate, and cost-effective alternative to traditional imaging methods.