Chest X‐ray image denoising method based on deep convolution neural network. Issue 11 (9th August 2019)

• Record Type: Journal Article
• Authors: Jin, Yan; Jiang, Xiao‐Ben; Wei, Zhen‐kun; Li, Yuan
• Is Part Of: IET image processing. Volume 13:Issue 11(2019)
• Page Start: 1970
• Page End: 1978
• Publication Date: 2019-08-09
• Subjects: convolution -- image denoising -- neural nets -- diseases -- learning (artificial intelligence); chest X‐ray image denoising method -- deep convolution neural network -- chest X‐ray images -- noise interference -- image denoising model -- neural network layers -- noisy X‐ray images -- depthwise separable convolution -- network model -- state‐of‐the‐art denoising algorithms -- denoising effects -- shorten; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0241
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.