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MPdeep: Medical Procession with Deep Learning

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Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

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Abstract

The thoracic diseases, can be regarded as one of the most serious and dangerous diseases, threat to the health of human being. Among these diseases, some of them may cause functional impairment and sequela in some degree and some dangerous ones may lead to death and organ failure or death. With the development of the artificial intelligence, the deep learning method can be utilized to deal with such issue. In this work, we proposed the MPdeep to demonstrate the medical imaging procession in the field. With such method, we can find out that the identical image quality of contrast enhanced chest CT, application of 40% DR reduced 48.5% radiation dose, and combination of 40% DR and 100 kV further reduce 58.9% radiation dose.

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Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 61133011.

Author information

Authors and Affiliations

  1. The Affiliated Hospital of Xuzhou Medical University, Xuzhou, People’s Republic of China

    Qi Liu

  2. The School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, People’s Republic of China

    Wenzheng Bao & Zhuo Wang

Authors
  1. Qi Liu
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  2. Wenzheng Bao
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  3. Zhuo Wang
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Corresponding author

Correspondence to Wenzheng Bao .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Liu, Q., Bao, W., Wang, Z. (2019). MPdeep: Medical Procession with Deep Learning. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_68

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  • DOI: https://doi.org/10.1007/978-3-030-26969-2_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26968-5

  • Online ISBN: 978-3-030-26969-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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