Authors :
Jageti Padmavathi; Bandari Akshitha; Bandi Sruthi; Potu Bhargavi; Ramavath Navya
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/418oBGi
DOI :
https://doi.org/10.5281/zenodo.7828389
Abstract :
The use of prenatal ultrasonography for fetal
assessment and the detection of abnormalities is on the
rise. This necessitates further anatomical studies of the
fetus. Ultrasound is able to identify most major structural
abnormalities in the developing fetus. Fifteen percent of
infants are born with minor abnormalities. A greater
incidence of even very modest birth defects is related with
an elevated chance of major abnormalities. A lot of
structural defects may be fixed if identified early, but
manual diagnosis is tedious, time-consuming, and errorprone. So, using a program might speed up the diagnostic
process and reduce the possibility of making a mistake.
Keywords :
Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Fetal Brain Ultrasound, Image Classification, Medical Imaging, Neural Networks, Obstetrics, Pregnancy Monitoring, VGG19 Algorithm.
The use of prenatal ultrasonography for fetal
assessment and the detection of abnormalities is on the
rise. This necessitates further anatomical studies of the
fetus. Ultrasound is able to identify most major structural
abnormalities in the developing fetus. Fifteen percent of
infants are born with minor abnormalities. A greater
incidence of even very modest birth defects is related with
an elevated chance of major abnormalities. A lot of
structural defects may be fixed if identified early, but
manual diagnosis is tedious, time-consuming, and errorprone. So, using a program might speed up the diagnostic
process and reduce the possibility of making a mistake.
Keywords :
Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Fetal Brain Ultrasound, Image Classification, Medical Imaging, Neural Networks, Obstetrics, Pregnancy Monitoring, VGG19 Algorithm.
