Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78472
Title: A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition
Authors: Yu, Z
Tan, EL
Ni, D
Qin, J 
Chen, SP
Li, SL
Lei, BY
Wang, TF
Keywords: Deep convolutional neural network
Standard plane recognition
Transfer learning
Ultrasound image
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE journal of biomedical and health informatics, May 2018, v. 22, no. 3, p. 874-885 How to cite?
Journal: IEEE journal of biomedical and health informatics 
Abstract: Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 x 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.
URI: http://hdl.handle.net/10397/78472
EISSN: 2168-2194
DOI: 10.1109/JBHI.2017.2705031
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