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

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

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

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

Contrastive Representation Learning for Ultrasound Videos

Machine learning project to predict fetal health from cardiotocography results

R mini project using Data Science and ML model

End-to-end clinical AI for fetal head circumference measurement — 4-phase pipeline: Residual U-Net (Dice 97.75%), Pseudo-LDDM v2 cine synthesis, temporal attention, structured pruning. Deployed on HuggingFace Spaces.

ML-powered clinical decision support system for maternal health risk and fetal health classification

Classification of fetal health status using cardiotocography (CTG) data via machine learning models

A machine learning and deep learning decision support system (XGBoost, RF, ANN) for classifying fetal health status using CTG data.

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

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

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…

A lightweight Tensorflow and Keras Model on detecting fetal braintumors in ultrasound images

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.

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

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.

Artificial Neural Network (ANN) project for multiclass fetal health classification using CTG data with TensorFlow, Keras, Dropout regularization, and Hyperparameter Tuning.