Chest X‐ray image denoising method based on deep convolution neural network

Jin, Yan; Jiang, Xiaoben; Wei, Zhen-kun; Li, Yuan

doi:10.1049/iet-ipr.2019.0241

Cited by 24 publications

(20 citation statements)

References 24 publications

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“…Jin et al used the deep convolution neural network to realize the denoising of the chest X-ray image. Through the residual learning of noise distribution, the convergence speed of the network model was effectively accelerated, and a good denoising effect was achieved [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

Zhang

Liu

2021

Computational Intelligence and Neuroscience

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In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

Zhang

Liu

2021

Computational Intelligence and Neuroscience

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“…Nevertheless, there are various types of noise in CXR images, such as ground-glass opacity, bilateral abnormalities, and interstitial abnormalities. Especially, low-dose CXR images susceptible to noise are complicated and fuzzy likely to interfere with the diagnosis of machines and doctors [ 20 ]. Therefore, obtaining clearer details in CXR images and improving the images quality by denoising is of great significance [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the high practical value, the medical image denoising method [ 20 , 23 – 27 ] has been extensively studied for a long time. Mondal et al [ 28 ] and Raj et al [ 29 ] used discrete wavelet technology [ 30 ] for medical image denoising.…”

Section: Introductionmentioning

confidence: 99%

“…Although traditional medical image denoising algorithms can improve the quality of medical images to a certain extent, they usually need to manually selected parameters and complex optimized algorithms [ 39 ], and enable to preserve texture details effectively [ 20 ]. Recently, deep learning methods [ 40 – 46 ], given enough data, have significant advances in images denoising than those traditional hand-crafted methods.…”

Section: Introductionmentioning

confidence: 99%

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Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN

Jiang

Zhu

Zheng

et al. 2021

Machine Vision and Applications

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Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.

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“…Since then various variants of CNNs have been proposed for different problems such as residual network (ResNet) [3], squeeze and excitation network (SENet) [4] for image classification; regions with CNN (R‐CNN) [5], Fast‐R‐CNN [6], Faster‐R‐CNN [7] for object detection; Mask‐R‐CNN [8] for image segmentation; local bit‐plane decoded Alexnet descriptor [9] for biomedical image retrieval; dual CNN [10] for depth estimation; HybridSN [11], genetic neural network [12] for hyperspectral image (HSI) classification; RCCNet [13] for colon cancer classification etc. The recent works over CNN are image classification [14], medical image analysis [15], deep hashing [16], HSI classification [17, 18], face anti‐spoofing [19, 20], texture classification [21] etc.…”

Section: Introductionmentioning

confidence: 99%

FuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classification

Roy¹,

Dubey

Chatterjee

et al. 2020

IET Image Processing

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Chest X‐ray image denoising method based on deep convolution neural network

Cited by 24 publications

References 24 publications

[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

[Retracted] Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN

FuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classification

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