Recognizing abnormality in fetal images using deep learning models

Abstract

The fetal health monitoring is essential for identifying any newlineintrauterine growth restriction and chromosomal disorder. The Down newlinesyndrome is a condition that occurs due to the misplacement of chromosome- newline21 causing abnormal fetal growth pattern. This may reflect in the physical newlineappearance and mental health of the child. The development of heart, brain, newlinespine and other parts of the fetal is monitored during periodic trimesters. newlineThus, the medical diagnosis and periodical treatment may save the baby from newlinedisease. The Ultrasonography is one of the best screening procedures for newlineidentifying the health condition of the fetus in detail. The fetal images newlineobtained from ultrasonography are used to analyze and predict abnormalities. newlineEarly prediction of obstetric care will reduce the fetal abnormal birth, thus the newlinemortality is reduced. newlineThe estimation of gestation age determines the fetal growth condition, newlinefetal size and fetal weight. In order to calculate the gestation age certain fetal newlinebiometric measurements are measured. The measurements of fetal abdominal newlinecircumference and the head measures can identify the fetal well being. newlineManual estimation of these essential parameters is operator dependent. newlineSeveral automatic machine learning model in the earlier research work faces newlinechallenges like over fitting due to low training data, segmentation problems in newlinehandling the irregular and low contrast ultrasound image, less accuracy in newlinefetal biometric parameter estimation and no robustness. A novel Deep newlineLearning method for predicting the abnormality in the fetus ultrasound images newlineis designed and implemented. newline