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Comprehensive Framework for Classification of Abnormalities in Brain MRI Using Neural Network

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Computational Statistics and Mathematical Modeling Methods in Intelligent Systems (CoMeSySo 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1047))

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Abstract

Precise identification of the abnormalities in brain is one of the challenging mechanisms to deal with in clinical diagnosis process. Irrespective of presence of various sophisticated diagnosis system in present time, there are frequent reporting of error-prone diagnosis even by the medical standards. Review of existing system toward detection of brain MRI shows that there are very less quantum of work being carried out towards emphasizing over classification process. This lead to formulate a novel framework in proposed system for assisting in classification process. The proposed system offers a simple and yet robust segmentation and classification process in multiple level which is further boosted by adopting Artificial Neural Network. The study outcome of the proposed system shows that it excels better accuracy in contrast to existing learning methods that are frequently used by researchers.

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Author information

Authors and Affiliations

  1. VTU, Belagavi, India

    S. Harish

  2. VTU PG-Centre, Mysuru, India

    G. F. Ali Ahammed

Authors
  1. S. Harish
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  2. G. F. Ali Ahammed
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Corresponding author

Correspondence to S. Harish .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Zdenka Prokopova

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Harish, S., Ali Ahammed, G.F. (2019). Comprehensive Framework for Classification of Abnormalities in Brain MRI Using Neural Network. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_8

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