Analysis of Ectopic Pregnancy in First Trimester Fetal Ultrasound Images using Machine Learning Technique

This study focuses on using medical imaging and machine learning to address Ectopic pregnancies (EPs) during the first trimester. The variability in ultrasound image quality and the presence of noise can hinder accurate image analysis in case of manual review by physicians, thus, this study aims to develop an automated system for fetal ultrasound image analysis. The research employs advanced image processing methods, including Region of Interest (ROI) selection and image enhancement techniques. Three classification algorithms CNN, SVM, and KNN are compared to select the most suitable one based on performance metrics. This suggests that CNN is a promising tool with 97% accuracy for the early detection of EP. The proposed system has the potential to transform the diagnosis and management of EP, potentially improving maternal outcomes. The goal is to enhance the precision and effectiveness diagnosis, potentially transforming early-stage pregnancy diagnostics.