Published Paper Details:

Fetal brain development plays a crucial role in determining overall health outcomes and potential neurological disorders. Accurate estimation of fetal brain age and detection of anomalies are essential for timely medical intervention and effective prenatal care. In this paper, we propose a robust ensemble learning approach for fetal brain age estimation and anomaly detection using advanced machine learning techniques. Our method leverages multiple learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract meaningful features from fetal brain images obtained through ultrasound imaging. Through a carefully designed ensemble framework, we integrate the predictions of individual models to enhance overall accuracy and robustness. Additionally, we introduce a novel anomaly detection mechanism based on anomaly scoring and thresholding techniques to identify deviations from normal brain development patterns. Experimental results on a large dataset demonstrate the effectiveness and robustness of our approach in accurately estimating fetal brain age and detecting anomalies with high precision and recall rates. Our proposed method holds significant promise for improving prenatal care and facilitating early detection and intervention of fetal brain abnormalities.