Abstract：Objective To propose a aluminum foil cap state detection method for blood collection tubes based on convolutional
neural networks, to realize the recognition of the state of the aluminum foil cap, aiming at the challenges of high identification
accuracy and speed requirements, diverse types of blood collection tubes, complicated state of aluminum foil cap state detection,
and the interference from liquid on tube walls in the automated biochemical immunoassay pipelines within medical laboratories.
Methods Firstly, a lightweight model design approach was adopted, which reduced the depth of the model to decrease the number
of parameters and computational requirements. Additionally, channel attention mechanism was introduced to enhance the feature
extraction capability of the samples. Moreover, Focal Loss was used to address the problem of mining difficult samples, further
optimizing the model’s performance. Finally, a teacher-student network was trained to perform knowledge distillation, resulting in
the final lightweight and compact model. Results The detection method due to the lightweight design of the student network model
was suitable for edge computing devices with limited resources. The parameter number of the model was only 0.354 M, the
computation amount was 0.165 GFlops, the recognition speed of the Jetson Nano device was 3.42 ms, and the recognition
accuracy reached 100% in the case of complex collection of blood vessels. Conclusion This study fully validates the lightweight,
efficient, and practical nature of the model, indicating that the detection method based on a lightweight convolutional neural networks
model can accurately identify the status of blood collection tube aluminum foil cap. It has become a solution for detecting the status
of blood collection tube aluminum foil caps in the automated biochemical immunoassay pipelines within medical laboratories.
侯剑平，赵万里，孙千鹏，王超，刘聪. 基于卷积神经网络的采血管铝箔帽
状态检测方法[J]. 中国医疗设备, 2024, 39(3): 7-13.
HOU Jianping, ZHAO Wanli, SUN Qianpeng, WANG Chao, LIU Cong. Aluminum Foil Cap State Detection Method for Blood Collection Tubes Based on
Convolutional Neural Networks. China Medical Devices, 2024, 39(3): 7-13.
