Digital radiography image denoising using a generative adversarial network

Sun, Yuan; Xi-ming, Liu; Cong, Peng; Li, Litao; Zhao, Zhongwei

doi:10.3233/xst-17356

Cited by 27 publications

(15 citation statements)

References 19 publications

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“…Fifthly, Noise is one key factor in digital radiography that is responsible for degrading the model performance. Consequently, in the preprocessing step, generative adversarial network (GAN) [ 66, 67 ], non-local mean filter [ 68 ], fuzzy genetic filter [ 69 ], robust navigation filter [ 70 ] based x-ray image denoising method can bring a significant improvement of the model performance. Finally, an application of “feature fusion” (or “ensemble learning”) technique to the best performing CNN models can enhance the final classification performance [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Rahaman

Yao

et al. 2020

XST

222

138

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BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE: One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS: Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS: A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

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Section: Discussionmentioning

confidence: 99%

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Rahaman

Yao

et al. 2020

XST

222

138

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“…In the field of orthognathic surgery, the use of ML can enhance the accuracy of diagnosis from maxillofacial images ( Sun et al, 2018 ; Zamora et al, 2012 ), assist in customizing the computer-aided design and manufacture (CAD/CAM) of orthodontic and surgical appliances and equipment ( Cevidanes, Styner & Proffit, 2006 ) and can be improved by comparing the results at finer intervals through image superposition ( Bouletreau et al, 2019 ).…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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“…Wu et al [6] studied a parallelized non-local means denoising method to suppress noise of CT images. Currently, deep learning theory has attracted attention in various imaging applications, which makes a better performance on CT image denoising [7][8][9][10][11]. Kang et al [12] proposed a deep CNN framework to solve CT image denoising problems, which combines a deep convolution neural network with a directional wavelet approach.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising and Ring Artifacts Removal for Spectral CT via Deep Neural Network

Ren

et al. 2020

IEEE Access

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Digital radiography image denoising using a generative adversarial network

Cited by 27 publications

References 19 publications

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Image Denoising and Ring Artifacts Removal for Spectral CT via Deep Neural Network

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