Effective and Efficient LDA+ELM Model for Supervised Classification of Brain Tumor Types Using 2D MRI Scans
DOI:
https://doi.org/10.21467/ias.9.1.160-173Abstract
Application of machine learning in multiclass classification of brain tumor types has contributed to the development of computer aided diagnosis (CAD) system that can potentially enhance accuracy and speed up diagnosis of the disease. LDA+ELM model with different activation functions were investigated to achieve the optimum performances in terms of accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. We also proposed a user-friendly GUI in characterizing brain tumor types using MR images. First, a total of 3064 slices of CE T1-weighted brain MR images with ground truth were downloaded from a free online database. The manually segmented tumor region was augmented and then undergo several feature extraction techniques. All the feature descriptors obtained were then concatenated, followed by LDA dimensionality approach. Performance of different number of LDA features and ELM activation functions were investigated by repeated training and test. The ELM output of training data for each class was used to fit GMM and these probabilistic models used to estimate posterior probabilities of test data. LDA+ELM model with 5 LDA feature input, utilizing sigmoid function as hidden nodes activation functions achieves the best generalization performance with accuracy of 98.92% and corresponding F-scores for meningioma, glioma and pituitary tumor of 97.81%, 99.1% and 99.5% respectively. The proposed method (LDA+ELM) model performs better compared to other previous works using the same dataset and performing the same classification task.
Keywords:
Multi-class classification of brain tumor, Linear Discriminant Analysis (LDA), Extreme Learning Machine (ELM), activation functions, Gaussian Mixture Model (GMM)Downloads
References
E. I. Zacharaki et al., "Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme," (in eng), Magnetic resonance in medicine, vol. 62, no. 6, pp. 1609-1618, 2009.
C. Parmar, J. D. Barry, A. Hosny, J. Quackenbush, and H. Aerts, "Data Analysis Strategies in Medical Imaging," (in eng), Clin Cancer Res, vol. 24, no. 15, pp. 3492-3499, Aug 1 2018.
R. J. Gillies, A. R. Anderson, R. A. Gatenby, and D. L. Morse, "The biology underlying molecular imaging in oncology: from genome to anatome and back again," (in eng), Clinical radiology, vol. 65, no. 7, pp. 517-521, 2010.
D. Mortazavi, A. Z. Kouzani, and H. Soltanian-Zadeh, "Segmentation of multiple sclerosis lesions in MR images: a review," (in eng), Neuroradiology, vol. 54, no. 4, pp. 299-320, Apr 2012.
N. Nabizadeh and M. Kubat, "Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features," Computers & Electrical Engineering, vol. 45, pp. 286-301, 2015/07/01/ 2015.
E. I. Zacharaki, V. G. Kanas, and C. Davatzikos, "Investigating machine learning techniques for MRI-based classification of brain neoplasms," (in eng), International journal of computer assisted radiology and surgery, vol. 6, no. 6, pp. 821-828, 2011.
J. Sachdeva, V. Kumar, I. Gupta, N. Khandelwal, and C. K. Ahuja, "Segmentation, feature extraction, and multiclass brain tumor classification," (in eng), Journal of digital imaging, vol. 26, no. 6, pp. 1141-1150, 2013.
P. Svolos et al., "Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques," (in eng), Magn Reson Imaging, vol. 31, no. 9, pp. 1567-77, Nov 2013.
K. M. Priya, S. Kavitha, and B. Bharathi, "Brain tumor types and grades classification based on statistical feature set using support vector machine," in 2016 10th International Conference on Intelligent Systems and Control (ISCO), 2016, pp. 1-8.
A. Jayachandran and R. Dhanasekaran, "MULTI CLASS BRAIN TUMOR CLASSIFICATION OF MRI IMAGES USING HYBRID STRUCTURE DESCRIPTOR AND FUZZY LOGIC BASED RBF KERNEL SVM," Iranian Journal of Fuzzy Systems, vol. 14, no. 3, pp. 41-54, 2017.
M. R. Ismael and I. Abdel-Qader, "Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network," in 2018 IEEE International Conference on Electro/Information Technology (EIT), 2018, pp. 0252-0257.
A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Neural Information Processing Systems, vol. 25, 01/01 2012.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 06/01 2014.
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," (in eng), Nature, vol. 521, no. 7553, pp. 436-44, May 28 2015.
Chuan Guo, Geoff Pleiss, Yu Sun, and K. Q. Weinberger, "On Calibration of Modern Neural Networks," in Proceedings of the 34 th International Conference on Machine Learning, PMLR 70, 2017., Sydney, Australia, 2017.
X. Jiang, M. Osl, J. Kim, and L. Ohno-Machado, "Calibrating predictive model estimates to support personalized medicine," (in eng), Journal of the American Medical Informatics Association : JAMIA, vol. 19, no. 2, pp. 263-274, Mar-Apr 2012.
B. Zadrozny and C. Elkan, Transforming classifier scores into accurate multiclass probability estimates. 2002, p. 694.
L. Cosmides and J. Tooby, "Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty," Cognition, vol. 58, no. 1, pp. 1-73, 1996/01/01/ 1996.
E. Eirola et al., "Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models," in Advances in Computational Intelligence, Cham, 2015, pp. 153-164: Springer International Publishing.
J. Cheng et al., "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition," (in eng), PLoS One, vol. 10, no. 10, p. e0140381, 2015.
J. Cheng et al., "Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation," (in eng), PLoS One, vol. 11, no. 6, p. e0157112, 2016.
W. Yang et al., "Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric," (in eng), Med Phys, vol. 39, no. 11, pp. 6929-42, Nov 2012.
S. A. Medjahed, A Comparative Study of Feature Extraction Methods in Images Classification. 2015, pp. 16-23.
I. Guyon, Andr, #233, and Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
L. Keyes and A. Winstanley, "Using moment invariants for classifying shapes on large-scale maps," Computers, Environment and Urban Systems, vol. 25, no. 1, pp. 119-130, 2001/01/01/ 2001.
H. Zhihu and L. Jinsong, "Analysis of Hu's moment invariants on image scaling and rotation," in 2010 2nd International Conference on Computer Engineering and Technology, 2010, vol. 7, pp. V7-476-V7-480.
A. Khotanzad and Y. H. Hong, "Invariant image recognition by Zernike moments," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 5, pp. 489-497, 1990.
D. v. P. P. Bhaskara Rao, Ch.pavan Kumar, "Feature extraction using zernike moments," International Journal of Latest Trends in Engineering and Technology vol. 2, no. 2, March 2013 2013.
C. Singh and R. Upneja, "Accurate calculation of high order pseudo-Zernike moments and their numerical stability," Digital Signal Processing, vol. 27, pp. 95-106, 2014/04/01/ 2014.
N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, vol. 1, pp. 886-893 vol. 1.
T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.
D. Cai, H. Bao, and X. He, "Sparse concept coding for visual analysis," in CVPR 2011, 2011, pp. 2905-2910.
Q. Gu, Z. Li, and J. Han, "Linear Discriminant Dimensionality Reduction," in Machine Learning and Knowledge Discovery in Databases, Berlin, Heidelberg, 2011, pp. 549-564: Springer Berlin Heidelberg.
H. Guang-Bin, "Learning capability and storage capacity of two-hidden-layer feedforward networks," IEEE Transactions on Neural Networks, vol. 14, no. 2, pp. 274-281, 2003.
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006/12/01/ 2006.
P. L. Bartlett, "The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network," IEEE Transactions on Information Theory, vol. 44, no. 2, pp. 525-536, 1998.
G.-B. Huang, X. Ding, and H. Zhou, "Optimization method based extreme learning machine for classification," Neurocomputing, vol. 74, no. 1, pp. 155-163, 2010/12/01/ 2010.
A. Hoerl and R. Kennard, "Ridge Regression: Biased Estimation for Nonorthogonal Problems," Technometrics, vol. 12, pp. 55-67, 04/09 2012.
G. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme Learning Machine for Regression and Multiclass Classification," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513-529, 2012.
G.-B. Huang, "Extreme Learning Machines (ELM); a survey. Int. J. Mach. Learn. & Cyber. 2, 107–122 (2011). https://doi.org/10.1007/s13042-011-0019-y
M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Information Processing & Management, vol. 45, no. 4, pp. 427-437, 2009/07/01/ 2009.
F. J. Provost, T. Fawcett, and R. Kohavi, "The Case against Accuracy Estimation for Comparing Induction Algorithms," presented at the Proceedings of the Fifteenth International Conference on Machine Learning, 1998.
Z. Zhang, "Introduction to machine learning: k-nearest neighbors," (in eng), Annals of translational medicine, vol. 4, no. 11, pp. 218-218, 2016.
W. Cao, J. Gao, Z. Ming, and S. Cai, "Some Tricks in Parameter Selection for Extreme Learning Machine," IOP Conference Series: Materials Science and Engineering, vol. 261, p. 012002, 10/01 2017.
S. Liu, L. Feng, Y. Xiao, and H. Wang, "Robust activation function and its application: Semi-supervised kernel extreme learning method," Neurocomputing, vol. 144, pp. 318-328, 2014/11/20/ 2014.
G. Parascandolo, H. Huttunen, and T. Virtanen, "Taming the waves: sine as activation function in deep neural networks," in ICLR, 2017.
N. Ghassemi, A. Shoeibi, and M. Rouhani, "Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images," Biomedical Signal Processing and Control, vol. 57, p. 101678, 2020/03/01/ 2020.
A. Kabir Anaraki, M. Ayati, and F. Kazemi, "Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms," Biocybernetics and Biomedical Engineering, vol. 39, no. 1, pp. 63-74, 2019/01/01/ 2019.
P. Afshar, A. Mohammadi, and K. Plataniotis, Brain Tumor Type Classification via Capsule Networks. 2018.
R. P. Sai Samarth, Apoorva Sikka, Abhinav Dhall and Deepti Bathula, "Dense and Diverse Capsule Networks:Making the Capsules Learn Better," Available: https://arxiv.org/pdf/1805.04001.pdf
A. Pashaei, H. Sajedi, and N. Jazayeri, "Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines," in 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), 2018, pp. 314-319.
Downloads
Published
Issue
Section
How to Cite
License
Copyright (c) 2020 Lim Jia Qi, Norma Alias
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Author(s) retains full copyright of their article and grants non-exclusive publishing right to International Annals of Science and its publisher "AIJR (India)". Author(s) can archive pre-print, post-print, and published version/PDF to any open access, institutional repository, social media, or personal website provided that Published source must be acknowledged with citation and link to publisher version.
Click here for more information on Copyright policy
Click here for more information on Licensing policy