Analysis of Machine and Deep Learning-Based Approaches in Fetal Brain Abnormality Detection Using Ultrasound Images

Background: Abnormalities in the brain are detected at an earlier stage using ultrasound imaging of fetuses. Some of the review works are explored only the old techniques that is not sufficient to detect the brain abnormality. Novelty of the survey: It aims to provide a deep comprehensive survey on fetal brain abnormality detection methods using deep learning and machine learning. Initially, the data acquisition of the fetal brain abnormality detection process is analyzed and the segmentation and feature extraction techniques utilized for the detection of fetal brain abnormalities are analyzed. Finally, the future scope of the fetal brain abnormality detection approaches using ultrasound images are discussed which may be helpful for the researchers to make further advancements. Findings: Most of the research works are explored with diverse performance measures. Instead, the accuracy of the Stack FBA and CNN model shows the accuracy of 97.27% among various research works.