Random Subspace Ensemble Artificial Neural Networks for First-episode Schizophrenia Classification
Roman Vyškovský, Daniel Schwarz, Eva Janoušová, Tomáš Kašpárek
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 317–321 (2016)
Abstract. Computer-aided schizophrenia diagnosis is a difficult task that has been developing for last decades. Since traditional classifiers have not reached sufficient sensitivity and specificity, another possible way is combining the classifiers in ensembles. In this paper, we take advantage of random subspace ensemble method and combine it with multi-layer perceptron (MLP) and support vector machines (SVM). Our experiment employs voxel-based morphometry to extract the grey matter densities from 52 images of first-episode schizophrenia patients and 52 healthy controls. MLP and SVM are adapted on random feature vectors taken from predefined feature pool and the classification results are based on their voting. Random feature ensemble method improved prediction of schizophrenia when short input feature vector (100 features) was used, however the performance was comparable with single classifiers based on bigger input feature vector (1000 and 10000 features).
- J. Sun, J. J. Maller, L. Guo, and P. B. Fitzgerald, “Superior temporal gyrus volume change in schizophrenia: a review on region of interest volumetric studies,” Brain Res. Rev., vol. 61, no. 1, pp. 14–32, Jun. 2009. [Online]. Available: http://dx.doi.org/10.1016/j.brainresrev.2009.03.004
- D. C. Glahn, A. R. Laird, I. Ellison-Wright, S. M. Thelen, J. L. Robinson, J. L. Lancaster, E. Bullmore, and P. T. Fox, “MetaAnalysis of Gray Matter Anomalies in Schizophrenia: Application of Anatomic Likelihood Estimation and Network Analysis,” Biol. Psychiatry, vol. 64, no. 9, pp. 774–781, Nov. 2008. [Online]. Available: http://dx.doi.org/10.1016/j.biopsych.2008.03.031
- C. Gaser, H.-P. Volz, S. Kiebel, S. Riehemann, and H. Sauer, “Detecting Structural Changes in Whole Brain Based on Nonlinear Deformations—Application to Schizophrenia Research,” NeuroImage, vol. 10, no. 2, pp. 107–113, Aug. 1999. [Online]. Available: http://dx.doi.org/10.1006/nimg.1999.0458
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1,” D. E. Rumelhart, J. L. McClelland, and C. PDP Research Group, Eds. Cambridge, MA, USA: MIT Press, 1986, pp.
- 318–362. J. Alirezaie, M. E. Jernigan, and C. Nahmias, “Neural network-based segmentation of magnetic resonance images of the brain,” IEEE Trans. Nucl. Sci., vol. 44, no. 2, pp. 194–198, Apr. 1997. [Online].
- Available: http://dx.doi.org/10.1109/23.568805 Y. Li, Z. Li, and Z. Xue, “Segmenting MR Images Using Fully-Tuned Radial Basis Functions (RBF),” in 9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV ’06, 2006, pp. 1–6. [Online]. Available: http://dx.doi.org/10.1109/ICARCV.2006.345425
- C. J. Savio A, “Neural classifiers for schizophrenia diagnostic support on diffusion imaging data,” Neural Netw. World, vol. 20, pp. 935–949, 2010.
- M. J. Jafri and V. D. Calhoun, “Functional classification of schizophrenia using feed forward neural networks,” Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. Suppl, pp. 6631–6634, 2006. [Online]. Available: http://dx.doi.org/10.1109/IEMBS.2006.260906
- C. Huang, B. Yan, H. Jiang, and D. Wang, “Combining Voxel-based Morphometry with Artifical Neural Network Theory in the Application Research of Diagnosing Alzheimer’s Disease,” in International Conference on BioMedical Engineering and Informatics, 2008. BMEI 2008, 2008, vol. 1, pp. 250–254. [Online]. Available: http://dx.doi.org/10.1109/BMEI.2008.245
- A. Savio, M. García-Sebastián, C. Hernández, M. Graña, and J. Villanúa, “Classification Results of Artificial Neural Networks for Alzheimer’s Disease Detection,” in Intelligent Data Engineering and Automated Learning - IDEAL 2009, E. Corchado and H. Yin, Eds. Springer Berlin Heidelberg, 2009, pp. 641–648. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-04394-9_78
- D. M. Joshi, N. K. Rana, and V. M. Misra, “Classification of Brain Cancer using Artificial Neural Network,” 2010, pp. 112–116. [Online]. Available: http://dx.doi.org/10.1109/ICECTECH.2010.5479975
- M. R. Arbabshirani, K. Kiehl, G. Pearlson, and V. D. Calhoun, “Classification of schizophrenia patients based on resting-state functional network connectivity,” Brain Imaging Methods, vol. 7, p. 133, 2013. [Online]. Available: http://dx.doi.org/10.3389/fnins.2013.00133
- T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832–844, Aug. 1998. [Online]. Available: http://dx.doi.org/10.1109/34.709601
- L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001. [Online]. Available: http://dx.doi.org/10.1023/A:1010933404324
- L. Breiman, “Bagging Predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, Aug. 1996. [Online]. Available: http://dx.doi.org/10.1023/A:1018054314350
- Y. Freund and R. E. Schapire, A Short Introduction to Boosting. 1999.
- J. J. Rodríguez, L. I. Kuncheva, and C. J. Alonso, “Rotation forest: A new classifier ensemble method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 10, pp. 1619–1630, Oct. 2006. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2006.211
- H. Yang, J. Liu, J. Sui, G. Pearlson, and V. D. Calhoun, “A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia,” Front. Hum. Neurosci., vol. 4, Oct. 2010. [Online]. Available: http://dx.doi.org/10.3389/fnhum.2010.00192
- E. Janousova, D. Schwarz, and T. Kasparek, “Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition,” Psychiatry Res. Neuroimaging, vol. 232, no. 3, pp. 237–249, Jun. 2015. [Online]. Available: http://dx.doi.org/10.1016/j.pscychresns.2015.03.004
- A. V. Lebedev, E. Westman, G. J. P. Van Westen, M. G. Kramberger, A. Lundervold, D. Aarsland, H. Soininen, I. Kłoszewska, P. Mecocci, M. Tsolaki, B. Vellas, S. Lovestone, and A. Simmons, “Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness,” NeuroImage Clin., vol. 6, pp. 115–125, 2014. [Online]. Available: http://dx.doi.org/10.1016/j.nicl.2014.08.023
- M. Liu, D. Zhang, and D. Shen, “Ensemble Sparse Classification of Alzheimer’s Disease,” Neuroimage, vol. 60, no. 2, pp. 1106–1116, Apr. 2012. [Online]. Available: http://dx.doi.org/10.1016/j.neuroimage.2012.01.055
- E. Janousova, D. Schwarz, G. Montana, and T. Kasparek, “Brain image classification based on automated morphometry and penalised linear discriminant analysis with resampling,” in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), 2015, pp. 263–268. [Online]. Available: http://dx.doi.org/10.15439/2015F147
- J. Ashburner and K. J. Friston, “Unified segmentation,” NeuroImage, vol. 26, no. 3, pp. 839–851, Jul. 2005. [Online]. Available: http://dx.doi.org/10.1016/j.neuroimage.2005.02.018
- M. Kubicki, M. E. Shenton, D. F. Salisbury, Y. Hirayasu, K. Kasai, R. Kikinis, F. A. Jolesz, and R. W. McCarley, “Voxel-Based Morphometric Analysis of Gray Matter in First Episode Schizophrenia,” NeuroImage, vol. 17, no. 4, pp. 1711–1719, Dec. 2002.
- J. Ashburner and K. J. Friston, “Voxel-based morphometry--the methods,” NeuroImage, vol. 11, no. 6 Pt 1, pp. 805–821, Jun. 2000. [Online]. Available: http://dx.doi.org/10.1006/nimg.2000.0582
- S. Lemm, B. Blankertz, T. Dickhaus, and K.-R. Müller, “Introduction to machine learning for brain imaging,” NeuroImage, vol. 56, no. 2, pp. 387–399, May 2011. [Online]. Available: http://dx.doi.org/10.1016/j.neuroimage.2010.11.004
- R. O. Duda, Pattern classification, 2nd ed. New York: Wiley, 2001.
- H. G. Schnack and R. S. Kahn, “Detecting Neuroimaging Biomarkers for Psychiatric Disorders: Sample Size Matters,” Neuroimaging Stimul., p. 50, 2016. [Online]. Available: http://dx.doi.org/10.3389/fpsyt.2016.00050