Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 2, 2021

Abstract

BACKGROUND: Diagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However, the efficiency of manual diagnosis is low, and high computational cost and poor convergence restrict the application of machine learning methods. This study aims to design a method that can reliably diagnose brain tumours from MRI scans.

Authors

  • Guoli Song
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang, CO, 110134, China. Electronic address: songgl@sia.cn.
  • Tian Shan
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Min Bao
    Shengjing Hospital of China Medical University, Shenyang 110011 China; Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang 110134, China.
  • Yunhui Liu
  • Yiwen Zhao
  • Baoshi Chen
    Department of Neuro-oncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. Electronic address: tiantanchenbaoshi@163.com.

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