Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.

Journal: BMC medical informatics and decision making
Published Date: Dec 24, 2024

Abstract

BACKGROUND: The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and poor generalizability, as they fail to capture the complete diversity of the data distribution. This study addresses these challenges by proposing a novel generative adversarial network (GAN) architecture tailored for semi-supervised lung nodule classification.

Authors

  • Ahmed Saihood
    College of Computer Science and Mathematics, University of Thi-Qar, Thi Qar, Iraq.
  • Wijdan Rashid Abdulhussien
    College of Computer Science and Mathematics, University of Thi-Qar, Thi Qar, Iraq.
  • Laith Alzubaid
    School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, 4000, Australia. l.alzubaidi@qut.edu.au.
  • Mohamed Manoufali
    CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia.
  • Yuantong Gu
    School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane City, Queensland, 4000, Australia.

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