Classification of cerebral microbleeds based on fully-optimized convolutional neural network

Jin Hong, Shui Hua Wang, Hong Cheng, Jie Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Cerebral microbleeds are important biomarkers of many cerebrovascular diseases and cognitive dysfunctions. Their distribution patterns can indicate some underlying aetiologies. Hitherto, few researches tried to detect cerebral microbleeds accurately and automatically. Some improvements have been achieved via traditional machine learning methods. In this paper, we proposed a method based on convolutional neural network (CNN) for further improving the performance. Firstly, sliding neighborhood processing method was applied to generate the input and target datasets based on 10 3D brain images of cerebral autosomal-dominant arteriopathy with subcortical infarcts and Leukoencephalopathy scanned by susceptibility-weighted imaging (SWI). Then, CNN was used to classify the cerebral microbleeds. To exert the full-power of convolutional neural network, almost all hyperparameters of CNN structure that could affect the performance were tested, such as the number of layers, type of activation function, pooling method, and filter size. A fully-optimized convolutional neural network structure for cerebral microbleeds classification was obtained. It performed better than four existed state-of-the-art approaches with a sensitivity of 99.74%, a specificity of 96.89% and an accuracy of 98.32%.

Original languageEnglish
Pages (from-to)15151-15169
Number of pages19
JournalMultimedia Tools and Applications
Volume79
Issue number21-22
DOIs
Publication statusPublished - 1 Jun 2020
Externally publishedYes

Keywords

  • Cerebral microbleeds
  • Convolutional neural network
  • Fully-optimized structure
  • Sliding neighborhood processing

Fingerprint

Dive into the research topics of 'Classification of cerebral microbleeds based on fully-optimized convolutional neural network'. Together they form a unique fingerprint.

Cite this