Enhanced Fetal brain Segmentation Using Multi-branch Transformer Dense net Architecture

The classification of fetal brain images in standard axial planes remains a critical challenge in prenatal diagnostics. Current techniques often depend on manual interpretation by radiologists, which is time-consuming and subject to variability. This reliance on human expertise introduces potential inconsistencies in diagnostic outcomes and limits scalability. Deep learning has shown promise as a powerful tool in automating medical image analysis, yet its feasibility in accurately differentiating between normal and abnormal fetal brain images remains under explored. This study aims to evaluate the effectiveness of advanced neural network architectures in classifying fetal brain sonographic images, focusing on improving diagnostic accuracy and efficiency. By leveraging convolution neural networks (CNN's) and other deep learning models, we seek to minimize human bias and variability, aiming for a consistent, objective evaluation of fetal brain health. Additionally, this work intends to address the practical challenges of deploying these algorithms in clinical settings, ensuring that they are both reliable and interpret able by medical practitioners. Through this research, we aim to contribute a robust, automated solution for fetal brain imaging, supporting radiologists in making more precise, timely diagnoses and ultimately enhancing prenatal care.