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Pulmonary nodules recognition based on parallel cross-convolution

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Abstract

Lung cancer is the highest incidence rate and mortality rate in human beings. Pulmonary nodules are the early manifestation of lung cancer. The accurate identification of pulmonary nodules is of great significance for the treatment and response of early lung cancer, and can effectively improve the life expectancy of lung cancer patients. In order to improve the recognition rate of lung cancer, this paper proposes a lung cancer recognition method based on parallel cross convolution neural network, which aims to solve the problems of gray scale classification, image edge blur, local body effect, artifact and noise in low-dose CT images. Firstly, the low-dose CT images are preprocessed. Then, the parallel cross convolution neural network model is used to classify pulmonary nodules. The model uses two data streams with different steps to extract image features, and is optimized by improved excitation function and batch regularization method. The experimental results show that the accuracy of pccnn proposed in this paper for pulmonary nodules or lung cancer is 97.78%. This shows that the combination of lung parenchyma cutting and parallel cross convolution neural network model can extract image features more deeply and improve the accuracy of lung cancer recognition.

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Acknowledgments

This work was supported by the National Natural Science Foundation in China (Grant No. 61703441).

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Authors and Affiliations

  1. College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China

    Yaowen Hu, Jialei Zhan, Guoxiong Zhou, Aibin Chen & Jiayong Li

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  1. Yaowen Hu
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Correspondence to Guoxiong Zhou.

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Hu, Y., Zhan, J., Zhou, G. et al. Pulmonary nodules recognition based on parallel cross-convolution. Multimed Tools Appl 81, 29137–29158 (2022). https://doi.org/10.1007/s11042-022-12908-x

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