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Cerebral Microbleeds Detection via Convolutional Neural Network with and Without Batch Normalization

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Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1014))

Abstract

Cerebral microbleeds (CMBs) as a kind of subclinical sign are associated with cerebrovascular and cognitive diseases, as well as normal aging. Hence, it is important to detect CMBs automatically and accurately for helping medical research and preventing related diseases. CMBs can be visualized as small and rounded radiological entities via susceptibility-weighted imaging (SWI). So far, some advances about detecting CMBs automatically have been achieved. In this study, a designed CNN structure is for further improving the performance of detecting CMBs automatically. Furthermore, a breakthrough technique named batch normalization (BN), which is widely used in training deep neural networks for accelerating the training process, was tested. The performance of CNN with BN was compared to that without BN. It is found that the latter model achieved better prediction results. Afterward, four state-of-the-art methods were compared to the designed CNN. The comparison shows the designed CNN achieved the best performance with a sensitivity of 99.69%, a specificity of 96.5%, and an accuracy of 98.09%.

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Acknowledgements

We have obtained the permission to use the data/image/sources from the competent authorities. We take responsibility for the same. Authors are thankful to Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Guangzhou, China for their support.

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Authors and Affiliations

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

    Jin Hong & Jie Liu

  2. Guangdong Provincial Key Laboratory of Mineral Resources& Geological Processes, 510275, Guangzhou, China

    Jie Liu

Authors
  1. Jin Hong
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  2. Jie Liu
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Corresponding author

Correspondence to Jie Liu .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Duy Tan University, Da Nang, Vietnam

    Bao Le Nguyen

  4. Graduate School, Duy Tan University, Da Nang, Vietnam

    Nhu Gia Nguyen

  5. Faculty of Information Technology, Hai Phong University, Hai Phong, Vietnam

    Dac-Nhuong Le

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Hong, J., Liu, J. (2020). Cerebral Microbleeds Detection via Convolutional Neural Network with and Without Batch Normalization. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1014. Springer, Singapore. https://doi.org/10.1007/978-981-13-9920-6_16

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