As considered to be less risky, less expensive, and more convenient than radiological examinations, ultrasound has been routinely employed in prenatal exams for the past decades. However, the quality of acquired ultrasound samples, i.e., ultrasound images or videos, and the further diagnosis is crucially depended on the sonographer. At the meantime, there are an extremely limited number of experienced sonographer available for the fetal ultrasound screening. Therefore, to reduce the workload of sonographers, and to promote the quality of fetal ultrasound screening, a deep convolutional neural network based framework is proposed for automatically differentiating five types of fetal head ultrasound standard planes, i.e., Transventricular plane (TV), Transthalamic plane (TT), Transcerebellar plane (TC), Coronal view of eyes (Eyes), Coronal view of nose (Nose), and other non-standard fetal head ultrasound images (Background). A dataset consists of 19928 fetal ultrasound images is applied for the model training and performance evaluation. By combining object detection network, object classification network, and model stacking technique, the proposed framework achieves the state-of-the-art performance with the average accuracy of 89.61% and the average F-1 score of 89.61%.