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2000
Volume 20, Issue 5
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

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.

Methods

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.

© 2025 Bentham Science Publishers
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2025-08-14

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Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning
Curr Bioinform 20, 443 (2025); https://doi.org/10.2174/0115748936295679240620094626
/content/journals/cbio/10.2174/0115748936295679240620094626
/content/journals/cbio/10.2174/0115748936295679240620094626
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  • Article Type:     Research Article
Keyword(s): Artificial intelligence; diagnostic automation; fetal development; medical diagnosis; obstetric ultrasound; ultrasound imaging

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