Enhanced pulmonary nodule detection with U-Net, YOLOv8, and swin transformer.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

RATIONALE AND OBJECTIVES: Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for early pulmonary nodule detection to improve patient outcomes. Current methods encounter challenges in detecting small nodules and exhibit high false positive rates, placing an additional diagnostic burden on radiologists. This study aimed to develop a two-stage deep learning model integrating U-Net, Yolov8s, and the Swin transformer to enhance pulmonary nodule detection in computer tomography (CT) images, particularly for small nodules, with the goal of improving detection accuracy and reducing false positives.

Authors

  • Xing Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Houde Wu
    School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
  • Longshuang Wang
    The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116011, China.
  • Jingxu Chen
    School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin, 300203, China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Xinliu He
    School of Medical Technology, Tianjin Medical University, Tianjin, 300203, China; School of Medical Imaging, Tianjin Medical University, Tianjin, 300203, China.
  • Ting Chen
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology (LMB), Guangdong Provincial Key Laboratory of Applied Marine Biology (LAMB), South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China. chan1010@scsio.ac.cn.
  • Minghui Wang
    College of Chemistry and Material Science, Shandong Agricultural University, Tai'an 271018, PR China.
  • Li Guo
    Department of Dental Implantology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University, Nanjing, China.

Keywords

External Resources

Popular Topics
Recent Journals