Abnormal Fetal Detection System
Keywords:
Abnormal Fetal Detection, Ultrasound Images, Deep Learning, Convolutional Neural Network (CNN), Medical Image Processing, Prenatal Care, Fetal Health Monitoring, Artificial Intelligence, Image Classification, Healthcare ManagementAbstract
The Abnormal Fetal Detection System is designed to assist healthcare professionals in the early detection of fetal
abnormalities using ultrasound imaging and advanced deep learning techniques. Early identification of fetal
health issues is essential for ensuring better maternal and neonatal outcomes. The proposed system employs a
Convolutional Neural Network (CNN) model to analyze ultrasound images and accurately classify fetal conditions
as either normal or abnormal.
The system provides a user-friendly interface through which doctors can upload ultrasound scans, enter patient
information, and obtain prediction results. It performs image preprocessing, feature extraction, and automated
classification to generate diagnostic reports that support clinical decision-making. Additionally, the application
ensures secure storage and management of patient records, promoting efficient healthcare services and data
accessibility.
By reducing manual effort and enhancing diagnostic accuracy, the system contributes to improved prenatal care,
timely medical intervention, and increased awareness of fetal health risks. The integration of artificial intelligence
in fetal monitoring offers a reliable and efficient solution for supporting obstetric diagnosis and healthcare
management.
