Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

Abstract

aims: This study aims to leverage artificial intelligence for enhancing medical diagnosis, focusing on ultrasound evaluation of fetal development and detection of fetal diseases. background: Traditional diagnostic methods in ultrasound are known for being time-consuming and laborious, prompting the need for more efficient approaches. objective: The objective of this research is to develop an end-to-end automatic diagnosis system using convolutional neural networks with ensemble learning to enhance robustness and accuracy in classifying ultrasound images. method: The study involves constructing and implementing the automatic diagnosis system, training it on a diverse dataset encompassing six categories: abdomen, brain, femur, thorax, maternal cervix, and other planes. result: Experimental results demonstrate that the proposed end-to-end system significantly improves the detection accuracy of the standard plane in ultrasound images. conclusion: The application of artificial intelligence through an ensemble learning-based automatic diagnosis system shows promise in advancing ultrasound-based medical diagnosis, particularly in fetal development assessment. other: This research contributes to the ongoing efforts in leveraging technology for more efficient and accurate medical diagnostic processes.