Infusion port level detection for intravenous infusion based on Yolo v3 neural network

Zeyong Huang; Yuhong Li; Tingting Zhao; Peng Ying; Ying Fan; Jun Li; Zeyong Huang; Yuhong Li; Tingting Zhao; Peng Ying; Ying Fan; Jun Li

doi:10.3934/mbe.2021175

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 3491-3501. doi: 10.3934/mbe.2021175

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Research article Special Issues

Infusion port level detection for intravenous infusion based on Yolo v3 neural network

Department of Anesthesiology, Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, China

Academic Editor: Kelvin K.L. Wong

Received: 28 February 2021 Accepted: 08 April 2021 Published: 21 April 2021

Purpose
In order to improve the accuracy of liquid level detection in intravenous left auxiliary vein infusion and reduce the pain of patients with blood returning from intravenous infusion, we propose a deep learning based liquid level detection model of infusion levels to facilitate this operation.
Method
We implemented a Yolo v3-based detection model of infusion level images in intravenous infusion, and at the same time, compare it with SURF image processing technique, RCNN, and Fast-RCNN methods.
Results
The model in this paper is better than the comparison algorithm in Intersection over Union (IoU), precision, recall and test time. The liquid level detection model based on Yolo v3 has a precision of 0.9768, a recall rate of 0.9688, an IoU of 0.8943, and a test time of 2.9 s.
Conclusion
The experimental results prove that the liquid level detection method based on deep learning has the characteristics of high accuracy and good real-time performance. This method can play a certain auxiliary role in the hospital environment and improve work efficiency of medical workers.
- deep learning,
- liquid level detection,
- Yolo v3,
- image processing,
- intravenous infusion
Citation: Zeyong Huang, Yuhong Li, Tingting Zhao, Peng Ying, Ying Fan, Jun Li. Infusion port level detection for intravenous infusion based on Yolo v3 neural network[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3491-3501. doi: 10.3934/mbe.2021175

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Abstract

Purpose

In order to improve the accuracy of liquid level detection in intravenous left auxiliary vein infusion and reduce the pain of patients with blood returning from intravenous infusion, we propose a deep learning based liquid level detection model of infusion levels to facilitate this operation.

Method

We implemented a Yolo v3-based detection model of infusion level images in intravenous infusion, and at the same time, compare it with SURF image processing technique, RCNN, and Fast-RCNN methods.

Results

The model in this paper is better than the comparison algorithm in Intersection over Union (IoU), precision, recall and test time. The liquid level detection model based on Yolo v3 has a precision of 0.9768, a recall rate of 0.9688, an IoU of 0.8943, and a test time of 2.9 s.

Conclusion

The experimental results prove that the liquid level detection method based on deep learning has the characteristics of high accuracy and good real-time performance. This method can play a certain auxiliary role in the hospital environment and improve work efficiency of medical workers.

References

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