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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis

Author 1: Kohei Arai Author 2: Yeni Herdiyeni Author 3: Hiroshi Okumura
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 3, No. 4 · Published 2012 · Cited by 22

DOI: 10.14569/IJACSA.2012.030416

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Abstract

Comparative study between 2D and 3D Local Binary Patter (LBP) methods for extraction from Computed Tomography (CT) imagery data in lung cancer diagnosis is conducted. The lung image classification is performed using probabilistic neural network (PNN) with histogram similarity as distance measure. The technique is evaluated on a set of CT lung images from Japan Society of Computer Aided Diagnosis of Medical Images. Experimental results show that 3D LBP has superior performance in accuracy compare to 2D LBP. The 2D LBP and 3D LBP achieved a classification accuracy of 43% and 78% respectively.

Keywords

lung cancer detection local binary pattern probabilistic neural network.

How to Cite this Article

Arai, K., Herdiyeni, Y., & Okumura, H. (2012). Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis. International Journal of Advanced Computer Science and Applications, 3(4). https://doi.org/10.14569/IJACSA.2012.030416

Arai, Kohei, et al.. "Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis." International Journal of Advanced Computer Science and Applications, vol. 3, no. 4, 2012, https://doi.org/10.14569/IJACSA.2012.030416.

@article{Arai2012,
  title     = {Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {4},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Yeni Herdiyeni and Hiroshi Okumura},
  doi       = {10.14569/IJACSA.2012.030416},
  url       = {https://doi.org/10.14569/IJACSA.2012.030416}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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Volume 17 No. 11, November 2026
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