Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

Baoping      Zhu; Fan      Yang; Hongliang      Duan; Zhipeng      Gao

doi:10.2174/0115748936295679240620094626

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Abstract

Introduction: The wide application of artificial intelligence in various fields has shown its potential to aid medical diagnosis. Ultrasound is an important tool used to evaluate fetal development and diagnose fetal diseases.

Method: However, traditional diagnostic methods are time-consuming and laborious. Therefore, we constructed an end-to-end automatic diagnosis system based on convolutional neural networks using ensemble learning to improve the robustness and accuracy of the system.

Results: The system classifies the ultrasound image dataset into six categories, namely, abdomen, brain, femur, thorax, maternal cervix, and other planes.

Conclusion: After experiments, the results showed that the proposed end-to-end system can considerably improve the detection accuracy of the standard plane.

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DOI https://dx.doi.org/10.2174/0115748936295679240620094626	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X

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Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

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