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| Automatic recognition of thalamic section in fetal two-dimensional ultrasound images based on convolutional neural network |
| DING Chun-xia1, QU Ruo-wei2, YIN Ying1 |
1．Ultrasound Department, Zhangjiakou Maternal and Child Health Hospital, Zhangjiakou Hebei 075000, China;
2.State Key Laboratory for Reliability and Intelligence of Electrical Equipment Co-constructed by
the Provincial Department of Electrical Engineering College of Hebei University of Technology,
Tianjin 300130, China |
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Abstract Objective: Automatic recognition and classification on fetal ultrasound image is significant to make doctors’ work efficiency. Method: Being different from traditional automatic classification method whose images should be segmented in detail, feature extracted manually and then classified, we proposed a deep convolutional neural network(CNN) based fetal thalamus plane ultrasound image recognition method. First, the images were pre-processed, such as image enhancement; then, we proposed an improved CNN algorithm. Result: This algorithm avoids the complex pre-processing of two-dimensional ultrasound image, and can input the original two-dimensional ultrasound image directly. It has strong capacity of adaption and generalization. The experimental results show that the recognition accuracy of this method can reach 94.81%. Conclusion: The proposed model provides a new reference for automatic medical image recognition technology.
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Received: 14 May 2019
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2020, Vol. 31 
