Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model.

Journal: European radiology
Published Date: Nov 30, 2022

Abstract

OBJECTIVES: Automatic bone lesions detection and classifications present a critical challenge and are essential to support radiologists in making an accurate diagnosis of bone lesions. In this paper, we aimed to develop a novel deep learning model called You Only Look Once (YOLO) to handle detecting and classifying bone lesions on full-field radiographs with limited manual intervention.

Authors

  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Sudong Li
    College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
  • Xiaoli Li
    State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Sheng Miao
    School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, China.
  • Cheng Dong
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: chengdong@qdu.edu.cn.
  • Chuanping Gao
    Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, Shandong, China.
  • Xuejun Liu
    Department of Radiology, Hospital Affiliated to Qingdao University, Qingdao, China.
  • Dapeng Hao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.
  • Wenjian Xu
    Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.
  • Mingqian Huang
    Department of Radiology, The Mount Sinai Hospital, New York, NY, 10029-0310, USA.
  • Jiufa Cui
    Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, 266003, Shandong, China. cuijiufa@qdu.edu.cn.

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