GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.

Journal: BMC pulmonary medicine
Published Date: Sep 20, 2024

Abstract

PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tackle these concerns, this investigation introduces a novel approach: the 3D Global Coordinated Attention Wide Inverted ResNet Network (GC-WIR). This network aims to achieve precise classification of benign and malignant pulmonary nodules, leveraging its merits of heightened efficiency, parsimonious parameterization, and robust stability.

Authors

  • Wenju Wang
    University of Shanghai for Science and Technology, Jungong 516 Rd, Shanghai, 200093, China.
  • Shuya Yin
    University of Shanghai for Science and Technology, Jungong 516 Rd, Shanghai, 200093, China. 223332934@st.usst.edu.cn.
  • Fang Ye
    Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
  • Yinan Chen
    12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China.
  • Lin Zhu
    Institute of Environmental Technology, College of Environmental and Resource Sciences; Zhejiang University, Hangzhou 310058, China.
  • Hong Yu
    University of Massachusetts Medical School, Worcester, MA.

Keywords

External Resources

Popular Topics
Recent Journals