Dual-Stage Deep Learning Approach for Fetal Plane Classification and Abnormality Detection in Ultrasound Images

This study presents a dual-stage deep learning framework for classifying fetal ultrasound images. The first stage involves identifying the fetus's anatomical plane, while the second stage classifies the fetal condition as normal, abnormal, or uncertain. The methodology includes image preprocessing using OpenCV and implementation of both traditional CNNs and Separable CNNs using TensorFlow and Keras. Experimental results demonstrate high classification accuracy with reduced inference time, with Separable CNNs outperforming traditional models. This approach offers promising support for automated and real-time prenatal diagnostics.