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Research Article | Open Access |

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Author 1: H. Mahersia Author 2: M. Zaroug Author 3: L. Gabralla
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 4, No. 4 · Published 2015 · Cited by 34

DOI: 10.14569/IJARAI.2015.040406

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Abstract

Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient’s chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

Keywords

Classification Computed Tomography Lung cancer Nodules Segmentation

How to Cite this Article

Mahersia, H., Zaroug, M., & Gabralla, L. (2015). Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques. International Journal of Advanced Research in Artificial Intelligence, 4(4). https://doi.org/10.14569/IJARAI.2015.040406

Mahersia, H., et al.. "Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques." International Journal of Advanced Research in Artificial Intelligence, vol. 4, no. 4, 2015, https://doi.org/10.14569/IJARAI.2015.040406.

@article{Mahersia2015,
  title     = {Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {4},
  number    = {4},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {H. Mahersia and M. Zaroug and L. Gabralla},
  doi       = {10.14569/IJARAI.2015.040406},
  url       = {https://doi.org/10.14569/IJARAI.2015.040406}
}

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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