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

Yuchen Zhu, Yi Gao, Meng Wang, Mei Li and Kun Wang

PLOS ONE, 2024, vol. 19, issue 9, 1-14

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305250 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 05250&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0305250

DOI: 10.1371/journal.pone.0305250

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().