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Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed

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Abstract

In order to detect the cerebral microbleed (CMB) voxels within brain, we used susceptibility weighted imaging to scan the subjects. Then, we used undersampling to solve the accuracy paradox caused from the imbalanced data between CMB voxels and non-CMB voxels. we developed a seven-layer deep neural network (DNN), which includes one input layer, four sparse autoencoder layers, one softmax layer, and one output layer. Our simulation showed this method achieved a sensitivity of 95.13%, a specificity of 93.33%, and an accuracy of 94.23%. The result is better than three state-of-the-art approaches.

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Acknowledgements

This paper was supported by NSFC (61602250), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Natural Science Foundation of Jiangsu Province (BK20150983), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Yu-Dong Zhang & Shui-Hua Wang

  2. Hunan Provincial Key Laboratory of Network Investigational Technology, Changsha, Hunan, 410138, China

    Yu-Dong Zhang

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

    Yu-Dong Zhang, Xiao-Xia Hou & Hong Chen

  4. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China

    Yin Zhang

  5. Department of Electrical Engineering, The City College of New York, CUNY, New York, NY, 10031, USA

    Shui-Hua Wang

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  1. Yu-Dong Zhang
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  3. Xiao-Xia Hou
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  4. Hong Chen
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Correspondence to Shui-Hua Wang.

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Yu-Dong Zhang, Yin Zhang, Xiao-Xia Hou and Hong Chen contributed equally to this paper

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Zhang, YD., Zhang, Y., Hou, XX. et al. Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimed Tools Appl 77, 10521–10538 (2018). https://doi.org/10.1007/s11042-017-4554-8

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