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

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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%.

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Acknowledgments

This paper was supported by International Program for Ph.D. Candidates, Sun Yat-Sen University and Natural Science Foundation of China (61602250), Henan Key Research and Development Project (182102310629).

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Authors and Affiliations

  1. School of Earth Sciences and Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Jin Hong & Jie Liu

  2. School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  3. Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China

    Hong Cheng

Authors
  1. Jin Hong
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  2. Shui-Hua Wang
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  3. Hong Cheng
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  4. Jie Liu
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Correspondence to Shui-Hua Wang or Jie Liu.

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Hong, J., Wang, SH., Cheng, H. et al. Classification of cerebral microbleeds based on fully-optimized convolutional neural network. Multimed Tools Appl 79, 15151–15169 (2020). https://doi.org/10.1007/s11042-018-6862-z

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  • DOI: https://doi.org/10.1007/s11042-018-6862-z

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