Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling

Shuihua Wang, Junding Sun, Irfan Mehmood, Chichun Pan, Yi Chen, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)

Abstract

Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods.

Original languageEnglish
Article numbere5130
JournalConcurrency and Computation: Practice and Experience
Volume32
Issue number1
DOIs
Publication statusPublished - 10 Jan 2020
Externally publishedYes

Keywords

  • cerebral micro-bleeding
  • convolution neural network
  • detection
  • intra-cerebral hemorrhage
  • stochastic pooling
  • stroke

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