Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network
LÜ Xiaoqi①② WU Liang② GU Yu② ZHANG Ming② LI Jing②
①(Inner Mongolia University of Technology, Hohhot 010051, China) ②(School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)
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.
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