Medical Image Quality Assessment Method based on Residual Learning

In clinical applications, the task of medical image quality assessment is done by radiologists and the existence of subjective factors leads to unstable the accuracy of the assessment. Deep learning methods bring the opportunity for objective assessment image quality. In order to avoid the overfitting problem of deep learning methods in small-sample data assessment, this paper proposes a medical image quality assessment method based on residual learning. This method adds a residual module to the traditional quality assessment network, so that the problem of overfitting can be effectively alleviated in the process of deepening the network structure. At the same time, this paper initially establishes a chest X-ray image quality assessment dataset. In the experiment, the correlation coefficients SRCC and PLCC of this method reaches 0.9692 and 0.9589, respectively, and the mean square error is 0.6697. Compared with the six assessment methods, three indicators are optimal. The method proposed in this paper can meet the clinical assessment needs of medical image quality, and the effectiveness of the method has been fully verified.