Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.

Journal: Medical science monitor : international medical journal of experimental and clinical research
PMID: 32555130

Abstract

BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build an ensemble deep learning classification model to accurately differentiate benign and malignant thyroid nodules. MATERIAL AND METHODS Based on current advanced methods of image segmentation and classification algorithms, we proposed an ensemble deep learning classification model for thyroid nodules (EDLC-TN) after precise localization. We compared diagnostic performance with four other state-of-the-art deep learning algorithms and three ultrasound radiologists according to ACR TI-RADS criteria. Finally, we demonstrated the general applicability of EDLC-TN for diagnosing thyroid cancer using ultrasound images from multi medical centers. RESULTS The method proposed in this paper has been trained and tested on a thyroid ultrasound image dataset containing 26 541 images and the accuracy of this method could reach 98.51%. EDLC-TN demonstrated the highest value for area under the curve, sensitivity, specificity, and accuracy among five state-of-the-art algorithms. Combining EDLC-TN with models and radiologists could improve diagnostic accuracy. EDLC-TN achieved excellent diagnostic performance when applied to ultrasound images from another independent hospital. CONCLUSIONS Based on ensemble deep learning, the proposed approach in this paper is superior to other similar existing methods of thyroid classification, as well as ultrasound radiologists. Moreover, our network represents a generalized platform that potentially can be applied to medical images from multiple medical centers.

Authors

  • Xi Wei
    Department of Diagnostic and Therapeutic Ultrasonography, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Ming Gao
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Ruiguo Yu
    College of Intelligence and Computing, Tianjin University, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin Key Laboratory of Advanced Networking, Tianjin, China (mainland).
  • Zhiqiang Liu
    Shenzhen Key Laboratory of Reproductive Immunology for Peri-implantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen, China.
  • Qing Gu
    Department of Ultrasonography, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province, Cangzhou, Hebei, China (mainland).
  • Xun Liu
    Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China. naturestyle@163.com.
  • Zhiming Zheng
    Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
  • Xiangqian Zheng
    Department of Ultrasonography, Integrated Traditional Chinese and Western Medicine Hospital, Jilin, China.
  • Jialin Zhu
    Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China (mainland).
  • Sheng Zhang
    Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, China.

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