Pregnancy Risk Factor Identification From Ultrasound Images using Deep Learning

This project utilizes deep learning (DL) to automate the identification of pregnancy risk factors from ultrasound scans., adding convenience and accuracy in prenatal diagnostics. This system finds multiple abnormalities., such as Arachnoid Cyst, Holoprosencephaly and various conditions. For analysis of large., labeled ultrasound datasets., the ResNet-based CNN model is used with the aid of advanced preprocessing and data augmentation for robust performance. This system significantly reduces the diagnostic workload on healthcare professionals by automating ultrasound image analysis., minimizing human error., and facilitating early risk identification for improved maternal and neonatal care.