V. Conclusion
In this work, a hybrid framework is designed for fetal head standard plane detection. The proposed framework composites of two parts, i.e., object detection network and object classification network. With the power of two networks, the designed framework is able to identify five types of standard planes with satisfactory performance. By introducing proposed model stacking, the performance of the proposed framework can be further improved. Comparing with the state-of-the-art of fetal ultrasound standard plane classification, i.e., SonoNet64, the average accuracy has been boosted to 0.8961. The average AUC is 0.9893 which is also indicated the effectiveness of the proposed hybrid framework. Since the experiments are designed to reproduce the scenario happened in the real life, the proposed method could be potentially applied to the automatic fetal screening and diagnosis.