Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Journal: Academic radiology
Published Date: Nov 25, 2019

Abstract

RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate its performance and usefulness for human reviewers.

Authors

  • Kum J Chae
    Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University, Biomedical Research Institute of Chonbuk National University Hospital, 634-18 Keumam-Dong, Jeonju, Jeonbuk 561-712, South Korea.
  • Gong Y Jin
    Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University, Biomedical Research Institute of Chonbuk National University Hospital, 634-18 Keumam-Dong, Jeonju, Jeonbuk 561-712, South Korea. Electronic address: gyjin@chonbuk.ac.kr.
  • Seok B Ko
    Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada.
  • Yi Wang
    Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Eun J Choi
    Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University, Biomedical Research Institute of Chonbuk National University Hospital, 634-18 Keumam-Dong, Jeonju, Jeonbuk 561-712, South Korea.
  • Hyemi Choi
    Department of Statistics and Institute of Applied Statistics, Chonbuk National University, Jeonju, South Korea.

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