On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

Musibau Adekunle Ibrahim; Oladotun Ayotunde Ojo; Peter Adefioye Oluwafisoye

doi:doi:10.11648/j.ijmi.20170506.12

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On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye

Published in International Journal of Medical Imaging (Volume 5, Issue 6)

Received: 19 December 2017 Accepted: 2 January 2018 Published: 19 January 2018

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Abstract

Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.

Published in	International Journal of Medical Imaging (Volume 5, Issue 6)
DOI	10.11648/j.ijmi.20170506.12
Page(s)	70-78
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Feature Selection, Multi-Fractal Descriptor, Classification Accuracy, Naïve Bayes, Bagged Decision Tree, Emphysema Patterns

References

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APA Style

Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. (2018). On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. International Journal of Medical Imaging, 5(6), 70-78. https://doi.org/10.11648/j.ijmi.20170506.12

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ACS Style

Musibau Adekunle Ibrahim; Oladotun Ayotunde Ojo; Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int. J. Med. Imaging 2018, 5(6), 70-78. doi: 10.11648/j.ijmi.20170506.12

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AMA Style

Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int J Med Imaging. 2018;5(6):70-78. doi: 10.11648/j.ijmi.20170506.12

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@article{10.11648/j.ijmi.20170506.12,
  author = {Musibau Adekunle Ibrahim and Oladotun Ayotunde Ojo and Peter Adefioye Oluwafisoye},
  title = {On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images},
  journal = {International Journal of Medical Imaging},
  volume = {5},
  number = {6},
  pages = {70-78},
  doi = {10.11648/j.ijmi.20170506.12},
  url = {https://doi.org/10.11648/j.ijmi.20170506.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20170506.12},
  abstract = {Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.},
 year = {2018}
}

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TY  - JOUR
T1  - On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images
AU  - Musibau Adekunle Ibrahim
AU  - Oladotun Ayotunde Ojo
AU  - Peter Adefioye Oluwafisoye
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ijmi.20170506.12
DO  - 10.11648/j.ijmi.20170506.12
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 70
EP  - 78
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20170506.12
AB  - Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.
VL  - 5
IS  - 6
ER  -

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Author Information

Musibau Adekunle Ibrahim

Department of Information and Communication Technology, Osun State University, Osogbo, Nigeria
Oladotun Ayotunde Ojo

Department of Physics, Osun State University, Osogbo, Nigeria
Peter Adefioye Oluwafisoye

Department of Physics, Osun State University, Osogbo, Nigeria

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Plain Text BibTeX RIS

APA Style

Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. (2018). On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. International Journal of Medical Imaging, 5(6), 70-78. https://doi.org/10.11648/j.ijmi.20170506.12

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ACS Style

Musibau Adekunle Ibrahim; Oladotun Ayotunde Ojo; Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int. J. Med. Imaging 2018, 5(6), 70-78. doi: 10.11648/j.ijmi.20170506.12

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AMA Style

Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int J Med Imaging. 2018;5(6):70-78. doi: 10.11648/j.ijmi.20170506.12

Copy | Download

@article{10.11648/j.ijmi.20170506.12,
  author = {Musibau Adekunle Ibrahim and Oladotun Ayotunde Ojo and Peter Adefioye Oluwafisoye},
  title = {On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images},
  journal = {International Journal of Medical Imaging},
  volume = {5},
  number = {6},
  pages = {70-78},
  doi = {10.11648/j.ijmi.20170506.12},
  url = {https://doi.org/10.11648/j.ijmi.20170506.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20170506.12},
  abstract = {Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images
AU  - Musibau Adekunle Ibrahim
AU  - Oladotun Ayotunde Ojo
AU  - Peter Adefioye Oluwafisoye
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ijmi.20170506.12
DO  - 10.11648/j.ijmi.20170506.12
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 70
EP  - 78
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20170506.12
AB  - Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.
VL  - 5
IS  - 6
ER  -

Copy | Download