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Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network

Author

Listed:
  • Xinying Zhang

    (The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Shanshan Kong

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China)

  • Yang Han

    (Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China)

  • Baoshan Xie

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Chunfeng Liu

    (The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China)

Abstract

To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.

Suggested Citation

  • Xinying Zhang & Shanshan Kong & Yang Han & Baoshan Xie & Chunfeng Liu, 2023. "Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1363-:d:1094053
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