Research Article
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Year 2016, Volume: 4 Issue: Special Issue-1, 170 - 174, 25.12.2016
https://doi.org/10.18201/ijisae.270422

Abstract

References

  • C. Güngen, T. Ertan, E. Eker, R. Yaşar and F. Engin (2002). Validity and reliability study on standardized mini mental test for the diagnosis of mild dementia in the Turkish society. Turkish Journal of Psychiatry. Vol.13. Pages. 273–281.
  • H.G. Kreeftenberg, E.L. Mooyaart, J.R. Huizenga and W.J. Sluiter (2000). Quantification of liver iron concentration with magnetic resonance imaging by combining T1-, T2-weighted spin echo sequences and a gradient echo sequence. Neth. J. Med. Vol.56. Pages. 133–137.
  • E. Westman, A. Simmons, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, M.W. Weiner, S. Lovestone, C. Spenger and L.O. Wahlund (2011). AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. Vol.58. Pages. 818–828.
  • R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.O. Habert, M. Chupin, H. Benali, O. Colliot, ADNI and others (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. Vol.56. Pages. 766–781.
  • J. Escudero, J.P. Zajicek and E. Ifeachor (2011). Machine Learning classification of MRI features of Alzheimer’s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, Pages. 7957–7960.
  • W.B. Jung, Y.M. Lee, Y.H. Kim and C.W. Mun (2015). Automated classification to predict the progression of Alzheimer’s disease using whole-brain volumetry and DTI. Psychiatry Investig. Vol.12. Pages. 92–102.
  • C. Aguilar, E. Westman, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, L. Kloszewska, H. Soininen, S. Lovestone, C. Spenger and others (2013). Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res. Neuroimaging. Vol.212. Pages. 89–98.
  • B. Fischl (2012). FreeSurfer. Neuroimage. Vol.62. Pages. 774–781.
  • A.M. Dale, B. Fischl and M.I. Sereno (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. Vol.9. Pages. 179–194.
  • B. Fischl, A. Liu and A.M. Dale (2001). Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. Med. Imaging, IEEE Trans. Vol.20. Pages. 70–80.
  • E.H.B.M. Gronenschild, P. Habets, H.I.L. Jacobs, R. Mengelers, N. Rozendaal, J. Van Os and M. Marcelis (2012). The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS One. Vol.7. Pages. e38234.
  • I. Rish (2001). An empirical study of the naive Bayes classifier. in IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol.3. Pages. 41–46.
  • C.J.C. Burges (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. Vol.2. Pages. 121–167.
  • M. Pei, E.D. Goodman, W.F. Punch and Y. Ding (1995). Genetic algorithms for classification and feature extraction. in Classification Society Conference.
  • A. Konak, D.W. Coit and A.E. Smith (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Saf. Vol.91. Pages. 992–1007.
  • R.S. Sexton, R.E. Dorsey and J.D. Johnson (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. Eur. J. Oper. Res. Vol.114. Pages. 589–601.

Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers

Year 2016, Volume: 4 Issue: Special Issue-1, 170 - 174, 25.12.2016
https://doi.org/10.18201/ijisae.270422

Abstract

Dementias are termed as neuropsychiatric disorders. Brain images of
dementia patients can be obtained through magnetic resonance imaging systems.
The relevant disease can be diagnosed by examining critical regions of those
images. Certain brain characteristics such as the cortical volume, the
thickness, and the surface area may vary among dementia types. These attributes
can be expressed as numerical values using image processing techniques. In this
study, the dataset involves T1 medical image sets of 63 samples. Each
particular sample is labeled with one of the three dementia types: Alzheimer's
disease, frontotemporal dementia, and vascular dementia. The image sets are
processed to create different feature groups. These are cortical volumes, gray
volumes, surface areas, and thickness averages. The main objective is seeking
brain sections more effective in establishing the clinical diagnosis. In other
words, searching an optimal feature subset process is carried out for each
feature group. To that end, a wrapper feature selection technique namely
genetic algorithm is used with Naive Bayes classifier and support vector
machines. The test phase is performed by using 10-fold cross validation. Consequently, accuracy results up to 93.7% with
different classifiers and feature selection parameters are shown.

References

  • C. Güngen, T. Ertan, E. Eker, R. Yaşar and F. Engin (2002). Validity and reliability study on standardized mini mental test for the diagnosis of mild dementia in the Turkish society. Turkish Journal of Psychiatry. Vol.13. Pages. 273–281.
  • H.G. Kreeftenberg, E.L. Mooyaart, J.R. Huizenga and W.J. Sluiter (2000). Quantification of liver iron concentration with magnetic resonance imaging by combining T1-, T2-weighted spin echo sequences and a gradient echo sequence. Neth. J. Med. Vol.56. Pages. 133–137.
  • E. Westman, A. Simmons, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, M.W. Weiner, S. Lovestone, C. Spenger and L.O. Wahlund (2011). AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. Vol.58. Pages. 818–828.
  • R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.O. Habert, M. Chupin, H. Benali, O. Colliot, ADNI and others (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. Vol.56. Pages. 766–781.
  • J. Escudero, J.P. Zajicek and E. Ifeachor (2011). Machine Learning classification of MRI features of Alzheimer’s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, Pages. 7957–7960.
  • W.B. Jung, Y.M. Lee, Y.H. Kim and C.W. Mun (2015). Automated classification to predict the progression of Alzheimer’s disease using whole-brain volumetry and DTI. Psychiatry Investig. Vol.12. Pages. 92–102.
  • C. Aguilar, E. Westman, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, L. Kloszewska, H. Soininen, S. Lovestone, C. Spenger and others (2013). Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res. Neuroimaging. Vol.212. Pages. 89–98.
  • B. Fischl (2012). FreeSurfer. Neuroimage. Vol.62. Pages. 774–781.
  • A.M. Dale, B. Fischl and M.I. Sereno (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. Vol.9. Pages. 179–194.
  • B. Fischl, A. Liu and A.M. Dale (2001). Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. Med. Imaging, IEEE Trans. Vol.20. Pages. 70–80.
  • E.H.B.M. Gronenschild, P. Habets, H.I.L. Jacobs, R. Mengelers, N. Rozendaal, J. Van Os and M. Marcelis (2012). The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS One. Vol.7. Pages. e38234.
  • I. Rish (2001). An empirical study of the naive Bayes classifier. in IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol.3. Pages. 41–46.
  • C.J.C. Burges (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. Vol.2. Pages. 121–167.
  • M. Pei, E.D. Goodman, W.F. Punch and Y. Ding (1995). Genetic algorithms for classification and feature extraction. in Classification Society Conference.
  • A. Konak, D.W. Coit and A.E. Smith (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Saf. Vol.91. Pages. 992–1007.
  • R.S. Sexton, R.E. Dorsey and J.D. Johnson (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. Eur. J. Oper. Res. Vol.114. Pages. 589–601.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Nihat Adar

Savaş Okyay This is me

Kemal Özkan

Suzan Şaylısoy

Belgin Demet Özbabalık Adapınar

Baki Adapınar This is me

Publication Date December 25, 2016
Published in Issue Year 2016 Volume: 4 Issue: Special Issue-1

Cite

APA Adar, N., Okyay, S., Özkan, K., Şaylısoy, S., et al. (2016). Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 170-174. https://doi.org/10.18201/ijisae.270422
AMA Adar N, Okyay S, Özkan K, Şaylısoy S, Özbabalık Adapınar BD, Adapınar B. Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):170-174. doi:10.18201/ijisae.270422
Chicago Adar, Nihat, Savaş Okyay, Kemal Özkan, Suzan Şaylısoy, Belgin Demet Özbabalık Adapınar, and Baki Adapınar. “Feature Selection on MR Images Using Genetic Algorithm With SVM and Naive Bayes Classifiers”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 170-74. https://doi.org/10.18201/ijisae.270422.
EndNote Adar N, Okyay S, Özkan K, Şaylısoy S, Özbabalık Adapınar BD, Adapınar B (December 1, 2016) Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 170–174.
IEEE N. Adar, S. Okyay, K. Özkan, S. Şaylısoy, B. D. Özbabalık Adapınar, and B. Adapınar, “Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 170–174, 2016, doi: 10.18201/ijisae.270422.
ISNAD Adar, Nihat et al. “Feature Selection on MR Images Using Genetic Algorithm With SVM and Naive Bayes Classifiers”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 170-174. https://doi.org/10.18201/ijisae.270422.
JAMA Adar N, Okyay S, Özkan K, Şaylısoy S, Özbabalık Adapınar BD, Adapınar B. Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:170–174.
MLA Adar, Nihat et al. “Feature Selection on MR Images Using Genetic Algorithm With SVM and Naive Bayes Classifiers”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 170-4, doi:10.18201/ijisae.270422.
Vancouver Adar N, Okyay S, Özkan K, Şaylısoy S, Özbabalık Adapınar BD, Adapınar B. Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):170-4.

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