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Computational Intelligence in Data Mining - Volume 1 pp 45–56Cite as

HOG Based Radial Basis Function Network for Brain MR Image Classification

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 31))

Abstract

Fully automated computer-aided diagnosis system is very much helpful for early detection and diagnosing of brain abnormalities like cancers and tumors. This paper presents two hybrid intelligent techniques such as HOG+PCA+RBFN and HOG+PCA+k-NN, which consists of four stages namely skull stripping, feature extraction, dimension reduction and classification. For efficient feature extraction Histograms of Oriented Gradients (HOG) method is used to extract the required feature vector and then the proposed techniques are used to classify images as normal or abnormal. The results show that the proposed technique gives an accuracy of 100 %, sensitivity of 99 % and specificity 100 %.

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Authors and Affiliations

  1. Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, Odisha, India

    N. K. S. Behera, M. K. Sahoo & H. S. Behera

Authors
  1. N. K. S. Behera
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  2. M. K. Sahoo
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  3. H. S. Behera
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Corresponding author

Correspondence to N. K. S. Behera .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Behera, N.K.S., Sahoo, M.K., Behera, H.S. (2015). HOG Based Radial Basis Function Network for Brain MR Image Classification. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_5

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  • DOI: https://doi.org/10.1007/978-81-322-2205-7_5

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2204-0

  • Online ISBN: 978-81-322-2205-7

  • eBook Packages: EngineeringEngineering (R0)

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