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Detecting cerebral microbleeds with transfer learning

  • Jin Hong
  • Hong Cheng
  • Yu-Dong ZhangEmail author
  • Jie LiuEmail author
Special Issue paper
  • 155 Downloads
Part of the following topical collections:
  1. Special Issue on Deep Learning Methods for Biomedical Information Analysis

Abstract

Cerebral microbleeds (CMBs) are small perivascular hemosiderin deposits leaked from cerebral small vessels in normal (or near normal) tissue. It is important to detect CMBs accurately and reliably for diagnosing and researching some cerebrovascular diseases and cognitive dysfunctions. In the last decade, several approaches based on traditional machine learning and classical convolutional neural network (CNN) were developed for detecting CMBs semi-automatically and automatically. In recent years, numerous advanced variants of CNN with deeper structure have been developed for image recognition, showing better performances comparing with classical CNN. In particular, ResNet proposed recently won the championships on many important image recognition benchmarks because of its extremely deep representations. In view of this, we proposed a method based on ResNet-50 for exploring the possibility of further improving the accuracy of CMBs detection in this study. Due to our small CMB samples size, transfer learning was employed. Based on the transfer learning of ResNet-50, we achieved a high performance with a sensitivity of 95.71 ± 1.044%, a specificity of 99.21 ± 0.076%, and an accuracy of 97.46 ± 0.524% in format of average ± standard deviation, which outperformed three state-of-the-art methods.

Keywords

Cerebral microbleeds detection Convolutional neural network ResNet-50 Transfer learning 

Notes

Acknowledgements

This paper is supported by National Natural Science Foundation of China (41574087), National Key Research and Development Plan (2017YFB1103202), Henan Key Research and Development Project (182102310629), and Natural Science Foundation of China (61602250).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Earth Sciences and EngineeringSun Yat-Sen UniversityGuangzhouPeople’s Republic of China
  2. 2.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  3. 3.Department of NeurologyFirst Affiliated Hospital of Nanjing Medical UniversityNanjingPeople’s Republic of China
  4. 4.Department of InformaticsUniversity of LeicesterLeicesterUK

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