Enhanced Classification of Endometrial Carcinoma Using Machine Learning Powered by Explainable AI

Abstract

Magnetic Resonance Imaging plays a vital part in diagnosis of mutant cells in the endometrial layer of women reproductive system. This Endometrial carcinoma (EC) is one among the type of uterine cancer acts as a major challenge for the medical practitioners for early diagnosis and classification. This study explores advanced imaging techniques and artificial intelligence (AI) for improved EC identification. The Methodology incudes, an approach of extracting texture features using  Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM) from the image data. These features are fused using a hybrid approach to capture complementary information. Machine learning classifiers including Random Forest (RF), Radial Basis Function (RBF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are trained and evaluated. Results demonstrate significant improvements in classification accuracy, with the hybrid feature extraction method achieving significant accuracy. Explainable AI (XAI) techniques are employed to interpret and visualize classifier decisions, providing details into the discriminative features contributing to EC classification.. The findings support the potential of integrating advanced imaging and machine learning, facilitated by XAI, for precise EC diagnosis and therapeutic planning.