A New Method in Improving the Accuracy of Fetal Brain Health Diagnosis Based on Image Analysis of Constrictors Feature in Ultrasound Images

Abstract

Fetal growth is a critical stage in prenatal care that requires timely identification of abnormalities in ultrasound images to protect the health of the fetus and mother. Ultrasound-based imaging has played an essential role in the diagnosis of fetal malformations and abnormalities, however, despite significant advances in ultrasound technology, accurate detection of abnormalities in prenatal images still poses significant challenges, often due to time and It requires considerable expertise from medical professionals. Methods based on machine learning are among the methods that help experts. In this research, a machine learning method based on image scattering features is presented for the classification of fetal brain ultrasound images. In the proposed method, after integration based on the maximum integration rule in scattered features based on discrete wavelet transform and discrete cosine transform, a method based on mutual information is presented for feature selection. The selected features have been used to diagnose the health status of the fetus with the help of the nearest neighbor. The aim is to perform a classification of several K classes of random forest support vector machine (SVM) and used for evaluation.