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An Intelligent Medical Image Understanding Method Using Two-Tier Neural Network Ensembles

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Innovations in Applied Artificial Intelligence (IEA/AIE 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3533))

Abstract

This paper proposes an intelligent medical image understanding method using a novel two-tier artificial neural network ensembles framework to identify lung cancer cells and discriminate among different lung cancer types by analyzing the chromatic images acquired from the microscope slices of needle biopsy specimens. In this way, each neural network takes the shape and color features extracting from lung cancer cell images as the inputs and all the five possible identification results as its output.

This research has been funded in part by the National Natural Science Foundation of P. R. China under grant No.39670714 and grant No.60273033.

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References

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Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, P. R. China

    Yubin Yang, Shifu Chen & Zhihua Zhou

  2. Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Yubin Yang & Hui Lin

  3. Nanjing Bayi Hospital, Nanjing, 210002, P. R. China

    Yukun Ye

Authors
  1. Yubin Yang
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  2. Shifu Chen
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  3. Zhihua Zhou
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  4. Hui Lin
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  5. Yukun Ye
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

  2. Dipartimento di Informatica, Università degli Studi di Bari,  

    Floriana Esposito

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© 2005 Springer-Verlag Berlin Heidelberg

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Yang, Y., Chen, S., Zhou, Z., Lin, H., Ye, Y. (2005). An Intelligent Medical Image Understanding Method Using Two-Tier Neural Network Ensembles. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_86

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  • DOI: https://doi.org/10.1007/11504894_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26551-1

  • Online ISBN: 978-3-540-31893-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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