General Practice Department, Huzhou Central Hospital, Huzhou 313003, China.
Affiliated Central Hospital Huzhou University, Huzhou 313000, China.
Comput Math Methods Med. 2022 Aug 24;2022:1987857. doi: 10.1155/2022/1987857. eCollection 2022.
In order to improve the standardization and accuracy of business process management of laboratory department in hospitals, combined with convolutional neural networks (CNN) and face recognition technology, an association application system of laboratory face recognition and test-tube barcode is designed by inputting patient's face and blood test-tube barcode into the system for storage. When the patient logs into the system again, the system uses the patient's face to automatically search for a matching test-tube barcode to obtain the test results. The simulation results show that the system can accurately recognize the face and match the corresponding test-tube barcode, and the accuracy and ROC of face recognition are 0.85 and 0.94, respectively. In addition, when the patient's face is within 5 m from the system camera, the accuracy of face recognition can reach 100%. It can be seen that the system designed in this paper shows good performance.
为了提高医院检验科业务流程管理的规范化和准确性，结合卷积神经网络（CNN）和人脸识别技术，通过将患者的面部和血试管条码输入系统进行存储，设计了一种检验科人脸识别和试管条码关联应用系统。当患者再次登录系统时，系统使用患者的面部自动搜索匹配的试管条码以获取测试结果。仿真结果表明，该系统能够准确识别面部并匹配相应的试管条码，人脸识别的准确率和 ROC 分别为 0.85 和 0.94。此外，当患者的面部距离系统摄像头 5m 以内时，人脸识别的准确率可达 100%。可以看出，本文设计的系统性能良好。
