1. Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing.
- Author
-
Zhu, Yuchen, Gao, Yi, Wang, Meng, Li, Mei, and Wang, Kun
- Subjects
- *
FETAL echocardiography , *COMPUTER performance , *EDGE computing , *CLINICAL medicine , *ARTIFICIAL intelligence , *DEEP learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF