The main goal at this point in the pregnancy is to track fetal growth in order to identify any problems. Due to inherent characteristics of the fetus, automatic fetal head segmentation and biometric assessment of HC (Head Circumference) from ultrasound pictures are regarded as tough challenges. The study suggests GAC Net (Graph Attention Convolutional Network), a novel Convolutional Neural Network (CNN) intended to address the aforementioned problems. In order to enhance communication between the encoder and decoder and lessen the effect of defects in ultrasound picture quality on HC measurement, it integrates a Graph Convolutional Network (GCN) module. To improve border area detection performance of the network, a novel attention mechanism is presented. The HC18 dataset of fetal head ultrasound images was used for the experiments.