Data Science Competition: “Automated Measurement of Fetal Head Circumference”. Top 8% Finalist.

A spatial feedback attention module (FBA) to enhance unsupervised 3D DLIR

Predicting Fetal Health, and Birth-Weight of fetus using Machine Learning

Machine learning project to predict fetal health from cardiotocography results

This project applies an Artificial Neural Network (ANN) to classify fetal health based on several health indicators

R mini project using Data Science and ML model

Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals and is a key indicator of human progress. The UN expects that by 2030, countries end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under‑5 mortality to at least as low as 25 per 1,000 l…

Fetal Health Classification- Model trained for high recall and precision value

Contrastive Representation Learning for Ultrasound Videos

Deployment repositories. For Original & Explained repositories, kindly visit link below:

This project uses a Random Forest classifier to categorize fetal health into Normal, Suspicious, or Pathological based on CTG data. With over 2,000 samples from Kaggle, the model achieves over 94% accuracy, helping support early detection of fetal health issues.

FetCAT: Automated fetal MRI plane classification using hybrid transformer-CNN architecture to assist in prenatal diagnosis and imaging analysis.

Decision Tree Machine Learning model for fetal health classification using Cardiotocography (CTG) data.

Machine learning-based fetal health classification system using cardiotocography data. Compares multiple algorithms (XGBoost, Random Forest, MLP, Logistic Regression) for predicting fetal health status with 95.9% accuracy.