• Jiyuan Wang, Zhenlin Li, Lixin Pu, Kai Zhang, Xiumin Liu, and Bin Zhou.
• School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China.
• Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Feb 25; 37 (1): 158-168.

AbstractWith the change of medical diagnosis and treatment mode, the quality of medical image directly affects the diagnosis and treatment of the disease for doctors. Therefore, realization of intelligent image quality control by computer will have a greater auxiliary effect on the radiographer's filming work. In this paper, the research methods and applications of image segmentation model and image classification model in the field of deep learning and traditional image processing algorithm applied to medical image quality evaluation are described. The results demonstrate that deep learning algorithm is more accurate and efficient than the traditional image processing algorithm in the effective training of medical image big data, which explains the broad application prospect of deep learning in the medical field. This paper developed a set of intelligent quality control system for auxiliary filming, and successfully applied it to the Radiology Department of West China Hospital and other city and county hospitals, which effectively verified the feasibility and stability of the quality control system.

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