Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network

Lü  Xiaoqi; WU Liang; GU Yu; ZHANG Ming; LI Jing

doi:10.11999/JEIT170769

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1353-1359

Lü Xiaoqi, WU Liang, GU Yu, ZHANG Ming, LI Jing. Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769

Citation:

Lü Xiaoqi, WU Liang, GU Yu, ZHANG Ming, LI Jing. Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1353-1359. doi: 10.11999/JEIT170769

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Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network

doi: 10.11999/JEIT170769

Funds:

The National Natural Science Foundation of China (61771266, 61179019), The Natural Science Foundation of the Inner Mongolia Autonomous region (2015MS0604), The Science and Technology Plan Projects of Baotou City (2015C2006-14), The Institutions of Higher Learning Scientific Research Projects of the Inner Mongolia Autonomous region (NJZY145)

Received Date: 2017-08-01
Rev Recd Date: 2017-12-01
Publish Date: 2018-06-19

Abstract

Abstract

In order to reduce the effect of low dose CT lung noise on the late diagnosis of lung cancer screening, a denoising model of low-dose CT lung based on deep convolution neural network is proposed. The input of the model is the complete CT lung image. The pooling layer reduces the dimension of input. Batch normalization works out the poor performance with the increase of network depth. The residuals of each layer are learned with residual learning. Finally, the denoised image is produced. Compared with classical methods, the proposed method achieves good filtering effect in solving the denoising method, and also retaining the details of lung image information, which is much better than the traditional filtering algorithm.
- Convolution Neural Network (CNN),
- Diagnosis,
- Lung denoising,
- Residual learning,
- Batch normalization

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References(32)

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