Abstract: Ultrasound examination during pregnancy is an important step to discover whether the fetus is abnormal,so it is of great value to carry out accurate and efficient examination and diagnosis on fetus.In this paper,bilinear convolutional neural network(BCNN) is used to identify the transverse section of the fetal head.On this basis,we propose BCNN-R and BCNN-S.The BCNN model takes the fetal craniocerebral ultrasound image as input,and firstly preprocesses the input data.Secondly,the two parallel sub-networks can extract the cross-sectional features with high identification and strong robustness from the input,after that the model integrates the extracted features,which is helpful to extract the fine features for recognition.Finally,the linear layer outputs the result of classification.In order to verify the effectiveness of the proposed algorithm,this paper makes contrast experiment on self-built fetal ultrasound dataset JTU19.The experimental results show that the proposed algorithms have ob-vious improvement on classification performance compared with the basic network(GoogleNet,DenseNet,SeNet,etc.),the overall accuracy of BCNN-S reaches 88.95%,and the precision and recall of BCNN-R in horizontal cross-sections achieves 97.22% and 88.61%.In addition,we also use the public dataset HC18 to conduct classification experiments.The accuracy,precision and recall of BCNN reach 89.48%,87.66% and 87.71% respectively,which further verifies the effectiveness of the proposed algorithms.

Key words: BCNN, Craniocerebrum image, Deep learning, Fetal ultrasound, Identification of transverse section