Open AccessChest X‐ray image denoising method based on deep convolution neural network
Author(s) -
Jin Yan,
Jiang XiaoBen,
Wei Zhenkun,
Li Yuan
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0241
Subject(s) - convolution (computer science) , noise reduction , computer science , noise (video) , artificial intelligence , residual , artificial neural network , computation , image (mathematics) , interference (communication) , reduction (mathematics) , deep learning , impulse noise , convergence (economics) , convolutional neural network , pattern recognition (psychology) , algorithm , channel (broadcasting) , pixel , mathematics , geometry , economics , economic growth , computer network
To improve the visual effect of chest X‐ray images and reduce the noise interference in disease diagnosis based on the chest X‐ray images, the authors proposed an image denoising model based on deep convolution neural network. They utilise batch normalisation to solve the problem of performance degradation due to the increase of neural network layers, and use residual learning of the distribution of noise in noisy X‐ray images. Specifically, the depthwise separable convolution is used to accelerate the convergence speed of network model, shorten the training time, and improve accuracy of the model. Compared to the several popular or the state‐of‐the‐art denoising algorithms, their extensive experiments demonstrate that their method can not only achieve better denoising effects, but also significantly reduce the complexity of the network and shorten the computation time.