Cerebral micro-bleeding detection based on densely connected neural network

Shuihua Wang, Chaosheng Tang, Junding Sun, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)

Abstract

Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

Original languageEnglish
Article number422
JournalFrontiers in Neuroscience
Volume13
Issue numberMAY
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Cmb detection
  • Cost matrix
  • Deep learning
  • Densenet
  • Transfer learning

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