  One of the routine examinations that are used for prenatal care in many
countries is ultrasound imaging. This procedure provides various information
about fetus health and development, the progress of the pregnancy and, the
baby's due date. Some of the biometric parameters of the fetus, like fetal head
circumference (HC), must be measured to check the fetus's health and growth. In
this paper, we investigated the effects of using multi-scale inputs in the
network. We also propose a light convolutional neural network for automatic HC
measurement. Experimental results on an ultrasound dataset of the fetus in
different trimesters of pregnancy show that the segmentation accuracy and HC
evaluations performed by a light convolutional neural network are comparable to
deep convolutional neural networks. The proposed network has fewer parameters
and requires less training time.
