Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.

Journal: World journal of surgical oncology
Published Date: Jan 8, 2019

Abstract

BACKGROUND: In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated.

Authors

  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Shujian Yang
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 1677 Wutaishan Street, Qingdao, 266555, Shandong Province, China.
  • Shan Yang
    Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
  • Cheng Zhao
    Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Guangye Tian
    Hisense Medical Equipment Co. Ltd, 399 Rd. Songling, Dist. Laoshan, Qingdao, 266000, Shandong Province, China.
  • Yuxiu Gao
    Department of Ultrasound, The Affiliated Hospital of Qingdao University, No. 1677 Wutaishan Street, Qingdao, 266555, Shandong Province, China.
  • Yongjian Chen
    Dermatology and Venereology Division, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
  • Yun Lu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.

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