Research on Intelligent Quality Control Method of DR Chest Film Based on Deep Learning
WANG Ping1,HU Boqi1,AN Donghong1,LIU Xulei1,SHI Zhangzhen1,TIAN Zhongsheng2,HAO Fude2,LIU Jingxin1
1. Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun Jilin 130021, China|
2. WX Medical Technology Co., Ltd., Shenyang Liaoning 110000, China
Abstract:Objective In order to reduce the burden of medical image quality control work on hospitals and related institutions, and make up for the various defects in manual quality control, this paper proposes a quality control method based on deep learning for DR radiography of Chest. Methods The semantic analysis of DR images was completed through a convolutional neural network with a specific structure, combined with medical image quality control standards, to automatically complete image quality control in batches. Results The average accuracy of the method in multiple data sets was 98.32%, and the average quality control time of a single image was 83 ms. Conclusion The model can realize automatic DR image quality control quickly and accurately.
王平1,胡博奇1,安东洪1,刘蓄蕾1,石张镇1,田中生2,郝富德2,刘景鑫1. 基于深度学习的DR胸片智能质控方法研究[J]. 中国医疗设备, 2020, 35(10): 28-33.
WANG Ping1,HU Boqi1,AN Donghong1,LIU Xulei1,SHI Zhangzhen1,TIAN Zhongsheng2,HAO Fude2,LIU Jingxin1. Research on Intelligent Quality Control Method of DR Chest Film Based on Deep Learning. China Medical Devices, 2020, 35(10): 28-33.