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Bio-ATT-CNN: A Novel Method for Identification of Glioblastoma

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

Through the years, many learning methods which made remarkable feat are raised in many industries. Many focus had been paid to attention-convolution (ATT-CNN). Achievements have been made in image processing, computer vision and natural language processing with this technique. But, insufficiency of interpretability is still a significant hinder to application of deep neural networks. It is especially in predicting performance of illness result. Regrettably, ATT-CNN is not able to directly apply in it. Accordingly, we came up with an original method. It is named Bio-ATT-CNN. It is able to distinguish long-term survival (LTS) and non-LTS if we use glioblastoma multiforme (GBM) as out detecting task. Let me just make a few points. Traditional model is not able to directly apply biological data. This model is able to be good for applying to biological data. It means that identifies essential biological connection of illness.

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Funding

This work was supported by National Natural Science Foundation of China (Grant nos. 62002189, 62102200), supported by Natural Science Foundation of Shandong Province, China (Grant nos. ZR2020QF038).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, Shandong, China

    Jinling Lai & Lin Yuan

  2. School of Computer and Software, Nanyang Institute of Technology, Changjiang Road 80, Nanyang, 473004, Henan, China

    Zhen Shen

Authors
  1. Jinling Lai
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  2. Zhen Shen
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  3. Lin Yuan
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Corresponding author

Correspondence to Lin Yuan .

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. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Lai, J., Shen, Z., Yuan, L. (2022). Bio-ATT-CNN: A Novel Method for Identification of Glioblastoma. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_69

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

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