Open Access   Article Go Back

Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier

K. O. Gupta1 , P. N. Chatur2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-11 , Page no. 350-353, Nov-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i11.350353

Online published on Nov 30, 2018

Copyright © K. O. Gupta, P. N. Chatur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: K. O. Gupta, P. N. Chatur, “Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.350-353, 2018.

MLA Style Citation: K. O. Gupta, P. N. Chatur "Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier." International Journal of Computer Sciences and Engineering 6.11 (2018): 350-353.

APA Style Citation: K. O. Gupta, P. N. Chatur, (2018). Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier. International Journal of Computer Sciences and Engineering, 6(11), 350-353.

BibTex Style Citation:
@article{Gupta_2018,
author = {K. O. Gupta, P. N. Chatur},
title = {Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2018},
volume = {6},
Issue = {11},
month = {11},
year = {2018},
issn = {2347-2693},
pages = {350-353},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3167},
doi = {https://doi.org/10.26438/ijcse/v6i11.350353}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i11.350353}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3167
TI - Multiclass Classification of fMRI using Linear Collaborative Discriminant Regression Classifier
T2 - International Journal of Computer Sciences and Engineering
AU - K. O. Gupta, P. N. Chatur
PY - 2018
DA - 2018/11/30
PB - IJCSE, Indore, INDIA
SP - 350-353
IS - 11
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
441 228 downloads 194 downloads
  
  
           

Abstract

In this paper, a hybrid GA-LCDRC model is proposed to address multiclass functional MRI classification problem. KNN based genetic algorithm is used as the feature selector and linear collaborative discriminant regression classifier (LCDRC) is used as the classifier. The effectiveness and usefulness of this model is assessed based on its classification specificity, sensitivity and accuracy. This approach is tested to Haxby’s 2001 functional MRI dataset with eight different classes. The result indicates that the proposed hybrid model can be used for multiclass cognitive state classification.

Key-Words / Index Term

fMRI, multiclass, linear collaborative discriminant regression classifier (LCDRC), genetic algorithm

References

[1] F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: A tutorial overview,” NeuroImage, Vol. 45, no. 1, pp. S199–S209, 2009.
[2] K. A. Norman, A. M. Polyn, G. J. Detre, and J. V. Haxby, “Beyond mind-reading: Multi-voxel pattern analysis of fMRI data”, Trends Cogn. Sci., Vol. 10, pp. 424–430, 2006.
[3] X. Qu, S. Kim, R. Cui, and H. J. Kim, “Linear collaborative discriminant regression classification for face recognition”, Journal of Visual Communication and Image Representation, Vol. 31, pp. 312-319, 2015.
[4] J.V. Haxby, M.I. Gobbini, M.L. Furey, A. Ishai, J.L. Schouten, and P. Pietrini, “Distributed and overlapping representations of faces and objects in ventral temporal cortex” Science, vol. 293, no. 5539, pp. 2425–2430, 2001.
[5] Yousefnezhad, Muhammad, and Daoqiang Zhang. "Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images" In International Conference on Brain Inspired Cognitive Systems, pp. 47-57. Springer, Cham, 2016.
[6] Kuncheva, Ludmila I., Juan J. Rodríguez, Catrin O. Plumpton, David EJ Linden, and Stephen J. Johnston. "Random subspace ensembles for fMRI classification" IEEE transactions on medical imaging 29, no. 2, pp 531-542, 2010.
[7] Halthor, A., & Kumar, K. A., “Prediction of visual perception from BOLD fMRI”, In Communication Systems, Computing and IT Applications (CSCITA), 2nd International Conference on (pp. 78-83). IEEE. 2017.
[8] Loula, J., Varoquaux, G., & Thirion, B, “Decoding fMRI activity in the time domain improves classification performance” NeuroImage, Vol 180, pp 203-210, 2017.
[9] Sun, X., Park, J., Kang, K., & Hur, J., “Novel hybrid CNN-SVM model for recognition of functional magnetic resonance images”, In Systems, Man, and Cybernetics (SMC), IEEE International Conference on (pp. 1001-1006). IEEE 2017.
[10] Shailaja K., Anuradha B., “Deep Learning Based Adaptive Linear Collaborative Discriminant Regression Classification for Face Recognition”. Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017.