Amniotic fluid and feetus growth detection using deep learning

Amniotic fluid is essential for fetal growth, provides cushioning, facilitates movement, and supports the growth of vital organs. Maintaining an ideal level of amniotic fluid is crucial, as abnormalities such as oligohydramnios (low fluid levels) and polyhydramnios (excessive fluid levels) can lead to significant pregnancy complications, including restricted fetal growth, preterm birth, and delivery challenges. Early detection and monitoring of amniotic fluid levels are vital for timely medical intervention and ensuring better maternal and fetal health outcomes.This study leverages a robust dataset of maternal and fetal health parameters, processed through advanced data cleaning and feature engineering techniques to enhance quality and relevance. The proposed system uses supervised learning models, includes deep learning algorithms to predict fluid level efficiently.The developed system demonstrates the ability to predict and classify fluid abnormalities, identifying conditions like oligohydramnios and polyhydramnios with significant precision. This approach offers a non-invasive, cost-effective, and efficient alternative to traditional diagnostic methods, and also enables healthcare providers to receive timely alerts and actionable insights.