Key result
A fusion detection framework for sports injury image features achieved a peak signal-to-noise ratio of 43 dB and an average detection time of 5.91 s, reducing error control to 0.011.
A novel multimedia 3D image fusion framework effectively detects sports injuries with high signal-to-noise ratio and low error.
May aid sports injury imaging workflows; leaves open prospective clinical validation.
This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.
No takes yet. Share an insight, caveat, or question.
Hongyu Liu (2023) studied Sports injuries. Fusion detection framework for sports injury image features was evaluated on Peak signal-to-noise ratio and average detection time. A fusion detection framework for sports injury image features achieved a peak signal-to-noise ratio of 43 dB and an average detection time of 5.91 s, reducing error control to 0.011.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: