Classification of Brain Tumor Grades by MRI Images using Artificial Neural Network

Document Type : Original Research (Full Papers)


1 Department of Electrical engineering,Biomedical engineering and computer, Faculty of Engineering , Qazvin Branch,Islamic Azad University, Qazvin ,Iran

2 Department of Electrical engineering, Biomedical engineering and computer, Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran


In recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative approach of the different classifiers to select the most appropriate classifier for identifying and extracting abnormal tissue and selected the best one by comparing their detection accuracies rate. In this research, GLCM and GLRM methods are used to extracting discriminating features. Thus results in they reduce the computational complexity. fuzzy entropy measurement method is used to determine the optimal properties and finally, we compared the four FFNN, MLP, BPNN, ANFIS neural networks to perform the decision making and classification process. The purpose of these four neural networks are to develop tools for discriminating the malignant tumors from benign ones assisting deciding in clinical diagnosis. Based on the results, we achieved high results among all classifiers. The proposed methodology results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. In our opinion, the use of these classifiers can be very useful in the diagnosis of brain tumors in MRI images. Our other goal is to prove the suitability of the ANN method as a valuable method for statistical methods. The novelty of the paper lies in the implementation of the proposed method for discriminating the malignant tumors from benign which results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. The efficiency of the method is proved through plenty of simulations and comparisons.


[1] A. S. Kurani, D. H. Xu, J. Furst, D. S. Raicu, Cooccurrence matrices for volumetric data. 7th IASTED
International Conference on Computer Graphics and Imaging, Kauai, vol.27, no.25, 2004.
[2] S. Chaplot, L. M. Patnaik, N. R. Jagannathan,Classification of magnetic resonance brain images using
wavelets as input to support vector machine and neural network. Biomedical signal processing and control,
vol.1, pp.86-92, 2006.
[3] K. Arthi and A. Tamilarasi, A Hybrid Fuzzy Model in Prediction of ADHD using Artificial Neural Networks.
Journal of Neural Systems Theory and Applications,vol.1, no.1, pp. 209-215, 2011.
[4] S. N. Deepa, B. Aruna Devi, Artificial Neural Networks design for Classification of Brain Tumour. International Conference on Computer Communication and Informatics, Coimbatore, INDIA, pp. 1-6, 2012.
[5] N. Varuna Shree, T. N. R. Kumar, Identification and classification of brain tumor MRI images with feature
extraction using DWT and probabilistic neural network. Brain informatics, vol.5, no.1, pp. 23-30,
[6] N. B. Bahadure, A. K. Ray, H. P. Thethi, Comparative approach of MRI-based brain tumor segmentation and
classification using genetic algorithm. Journal of digital imaging, vol.31, no.4, pp. 477-489, 2018.
[7] T. Pandiselvi, R. Maheswaran, Efficient Framework for Identifying, Locating, Detecting and Classifying MRI
Brain Tumor in MRI Images. Journal of medical systems, vol.43, no.7, pp. 189, 2019.
[8] M. Jafari, Sh. Kasaei, Automatic Brain Tissue Detection in MRI Images Using Seeded Region Growing
Segmentation and Neural Network Classification.Australian Journal of Basic and Applied Sciences,vol.5, no.8, pp. 1066-1079, 2011.
[9] A. Demirhan, I. Güler, Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Engineering Applications of Artificial Intelligence, vol.24, no.2, pp. 358-367, 2011.
[10] M.C. Clark, L.O. Hall, D.B. Goldgof, R. Velthuizen,F.R. Murtagh, M.S. Silbiger, Automatic Tumor
Segmentation Using Knowledge-Based Techniques.IEEE Transactions On Medical Imaging, vol.17, no.2,
pp. 187-201, 1998.
[11] E. S. A. El-Dahshan, T. Hosny, A. B. M. Salem,Hybrid intelligent techniques for MRI brain images
classification. Digital Signal Processing, vol.20, no.2,pp. 433-441, 2010.
[12] A. E. Lashkari, A Neural Network based Method for Brain Abnormality Detection in MR Images Using
Gabor Wavelets. International Journal of Computer Applications, vol.4, no.7, 2010.
[13] M. S. Kalas, An artificial neural network for detection of biological early brain cancer. International Journal of Computer Applications, vol.1, no.6, pp. 17-23,2010.
[14] X. Xuan, Q. Liao, Statistical structure analysis in MRI brain tumor segmentation. In Fourth International
Conference on Image and Graphics (ICIG 2007),Sichuan, China, pp. 421-426, 2007.
[15] D. J. hemanthl, D. Selvathi, J. Anitha, Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image
Segmentation. IEEE International Advance Computing Conference, Patiala, India, pp. 609-614,2009.
[16] P. Mohanaiah, P. Sathyanarayana, L. GuruKumar,Image texture feature extraction using GLCM
approach. International journal of scientific and research publications, vol.3, no.5. pp. 1, 2013.
[17] K. D. Kharat, P. P. Kulkarni, M. B. Nagori, Brain tumor classification using neural network based
methods. International Journal of Computer Science and Informatics, vol.1, no.4, pp. 2231-5292, 2012.
[18] Sh. Shadro, R. Ma'aref Dost, M. Yaghoobi, H. R.Pourreza, Splitting images using multifractal estimation,
entropy and fuzzy clustering. First Joint Congress on Fuzzy and Intelligent Systems, Mashhad, Iran, 2007.
Volume 12, Issue 2
December 2019
Pages 1-11
  • Receive Date: 13 August 2019
  • Revise Date: 14 May 2020
  • Accept Date: 07 November 2020
  • First Publish Date: 07 November 2020