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CMB-net: a deep convolutional neural network for diagnosis of cerebral microbleeds

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Abstract

Cerebral microbleed (CMB) is related to cerebral vascular diseases. In this paper, we propose the use of deep convolutional neural network to implement CMB automatic diagnosis based on brain susceptibility-weighted images (SWIs). First of all, a sliding neighborhood method was employed to get 13,031 samples for training and testing. Then, an 18-layer CMB-Net was designed to classify the samples as CMB or non-CMB. The CMB-Net was trained by RMSprop based on the five-fold cross- validation. The total running time of the five-fold cross-validation was merely 184.79 s, and the average testing accuracy reached 98.39%, which was better than several recently published methods. The results suggested that our CMB-Net was accurate in detecting CMB.

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Acknowledgments

The paper is supported by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), British Heart Foundation Accelerator Award, UK; Fundamental Research Funds for the Central Universities (CDLS-2020-03); Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education.

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Authors and Affiliations

  1. School of Education Science, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Zhihai Lu

  2. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Yan Yan & Shui-Hua Wang

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  1. Zhihai Lu
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  2. Yan Yan
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  3. Shui-Hua Wang
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Correspondence to Shui-Hua Wang.

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Lu, Z., Yan, Y. & Wang, SH. CMB-net: a deep convolutional neural network for diagnosis of cerebral microbleeds. Multimed Tools Appl 81, 19195–19214 (2022). https://doi.org/10.1007/s11042-021-10566-z

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  • DOI: https://doi.org/10.1007/s11042-021-10566-z

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