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Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing

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  • Yuchen Zhu
  • Yi Gao
  • Meng Wang
  • Mei Li
  • Kun Wang

Abstract

Recent breakthroughs in medical AI have proven the effectiveness of deep learning in fetal echocardiography. However, the limited processing power of edge devices hinders real-time clinical application. We aim to pioneer the future of intelligent echocardiography equipment by enabling real-time recognition and tracking in fetal echocardiography, ultimately assisting medical professionals in their practice. Our study presents the YOLOv5s_emn (Extremely Mini Network) Series, a collection of resource-efficient algorithms for fetal echocardiography detection. Built on the YOLOv5s architecture, these models, through backbone substitution, pruning, and inference optimization, while maintaining high accuracy, the models achieve a significant reduction in size and number of parameters, amounting to only 5%-19% of YOLOv5s. Tested on the NVIDIA Jetson Nano, the YOLOv5s_emn Series demonstrated superior inference speed, being 52.8–125.0 milliseconds per frame(ms/f) faster than YOLOv5s, showcasing their potential for efficient real-time detection in embedded systems.

Suggested Citation

  • Yuchen Zhu & Yi Gao & Meng Wang & Mei Li & Kun Wang, 2024. "Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0305250
    DOI: 10.1371/journal.pone.0305250
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