Broofer, A., A. Rezaeyan, and S. Shokoohyar. (2016), Identifying the customer behavior model in life insurance Sector using data mining. Management Research in Iran. 20(4): p. 65-94.
 Khadivar, A. and F. Majibian. (2018), Workshops Clustering Using a Combination Approach of Data Mining and MCDM. Modern Researches in Decision Making. 3(2): p. 107-128.
 Ramakrishnan, T. and B.J.P.R.L. Sankaragomathi. (2017), A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation. 94: p. 163-171.
 Zhang, N., et al. (2011), Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Computer Vision and Image Understanding. 115(2): p. 256-269.
 Ortiz, A., et al. (2013), Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Applied Soft Computing. 13(5): p. 2668-2682.
 Mohan, G. and M.M. Subashini. (2018), MRI based medical image analysis: Survey on brain tumor grade classification. Biomedical Signal Processing and Control. 39: p. 139-161.
 Nabizadeh, N. and M. Kubat. (2015), Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Computers & Electrical Engineering. 45: p. 286-301.
 Loizou, C.P., et al. (2009), Brain MR image normalization in texture analysis of multiple sclerosis. Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on: p. 1-5.
 Haralick, R.M. and K. Shanmugam. (1973), Textural features for image classification. IEEE Transactions on systems, man, and cybernetics,(6): p. 610-621.
 Daubechies, I.(1992), Ten lectures on wavelets. SIAM.
 Zöllner, F.G., K.E. Emblem, and L.R. Schad. (2012), SVM-based glioma grading: optimization by feature reduction analysis. Zeitschrift für medizinische Physik. 22(3): p. 205-214.
 Dash, M. and H. Liu. (1997), Feature selection for classification. Intelligent data analysis. 1(1-4): p. 131-156.
 Dean, B.L., et al. (1990), Gliomas: classification with MR imaging. Radiology. 174(2): p. 411-415.
 Chaplot, S., L. Patnaik, and N. Jagannathan. (2006), Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control. 1(1): p. 86-92.
 El-Dahshan, E.-S.A., T. Hosny, and A.-B.M. Salem. (2010), Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing. 20(2): p. 433-441.
 Marshkole, N., B.K. Singh, and A. Thoke. (2011), Texture and shape based classification of brain tumors using linear vector quantization. International Journal of Computer Applications. 30(11): p. 21-23.
 Preethi, G. and V. Sornagopal. (2014), MRI image classification using GLCM texture features. 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE): p. 1-6.
 Nayak, D.R., R. Dash, and B. Majhi. (2016), Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing. 177: p. 188-197.
 Vogado, L.H., et al. (2018), Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence. 72: p. 415-422.
 Hassan zadeh, A., M.h. Ghanbari, and S. Elahi. (2012), Classification of mobile banking users by data mining approach: Comparison between artificial neural networks and naïve bayes techniques. Management Research in Iran. 16(2): p. 57-71.
 Krizhevsky, A., I. Sutskever, and G.E. Hinton. (2012), Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems: p. 1097-1105.
 Szegedy, C., et al. (2015), Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition: p. 1-9.
 Tipping, M.E. and C.M. Bishop. (1999), Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 61(3): p. 611-622.
 Bae, M.H., T. Wu, and R. Pan. (2010), Mix-ratio sampling: Classifying multiclass imbalanced mouse brain images using support vector machine. Expert Systems with Applications. 37(7): p. 4955-4965.
 Cheng, J. (brain tumor dataset. School of Biomedical Engineering Southern Medical University, Guangzhou, China. https:// github.com/ chengjun583/ brainTumorRetrieval.