Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

Journal: The oncologist
Published Date:

Abstract

BACKGROUND: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.

Authors

  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Xing Sun
    1 Division of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, P. R. China.
  • Kang Dang
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Xiao-Wei Guo
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Jia Chang
    Tencent, Shenzhen, People's Republic of China.
  • Zong-Qiao Yu
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Fei-Yue Huang
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Yun-Sheng Wu
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Zhu Liang
    Tencent Youtu Lab, Shanghai, People's Republic of China.
  • Zai-Yi Liu
    Department of Radiology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
  • Xue-Gong Zhang
    MOR Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & System Biology, Department of Automation, Tsinghua University, Beijing, People's Republic of China.
  • Xing-Lin Gao
    Department of Respiration, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
  • Shao-Hong Huang
    The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
  • Jie Qin
    The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
  • Wei-Neng Feng
    First People's Hospital of Foshan, Foshan, People's Republic of China.
  • Tao Zhou
    Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yan-Bin Zhang
    Guangzhou Chest Hospital, Guangzhou, People's Republic of China.
  • Wei-Jun Fang
    Guangzhou Chest Hospital, Guangzhou, People's Republic of China.
  • Ming-Fang Zhao
    Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, People's Republic of China.
  • Xue-Ning Yang
    Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
  • Qing Zhou
    Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Yi-Long Wu
    Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China. Electronic address: syylwu@live.cn.
  • Wen-Zhao Zhong
    Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China 13609777314@163.com syylwu@live.cn.