A Review on Brain Tumor Classification Methodologies

Authors

  • Bichitra Panda  Department of Computer Science and Application, Sambalpur University, Burla, Odisha, India
  • Dr. Chandra Sekhar Panda  Department of Computer Science and Application, Sambalpur University, Burla, Odisha, India

DOI:

https://doi.org/10.32628/IJSRST20717

Keywords:

Brain Tumor, DWT, Feature Extraction, Feature Reduction MRI, MRI Classification.

Abstract

Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.

References

  1. A. Kharrat, Karim G., M. B. Messaoud, Nacéra B. and Mohamed A. 2010. A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine, Leonardo Journal of Sciences Issue 17, July-December 2010, ISSN 1583-0233
  2. V.P.Gladis Pushpa Rathi and Dr.S.Palani. 2012. A novel approach for feature extraction and selection on mri images for brain tumor classification. David C. Wyld, et al. (Eds): CCSEA, SEA, CLOUD, DKMP, CS & IT 05, pp. 225–234, 2012,DOI : 10.5121/csit.2012.2224
  3. Chaplot, S., Patnaik, L. M., & Jagannathan, N. R. 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), 86–92. doi:10.1016/j.bspc.2006.05.002
  4. Ravikumar Gurusamy and Dr Vijayan Subramaniam. 2017. A Machine Learning Approach for MRI Brain Tumor Classification,CMC, vol.53, no.2, pp.91-108, 2017
  5. Zhang, Y., & Wu, L. 2012. An mr brain images classifier via principal component analysis and kernel support vector machine. Progress In Electromagnetics Research, 130, 369–388. doi:10.2528/pier12061410
  6. El-Dahshan, E.-S. A., Hosny, T., & Salem, A.-B. M. 2010. Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing, 20(2), 433–441. doi:10.1016/j.dsp.2009.07.002
  7. Zhang, Y., Wang, S., & Wu, L. 2010. A novel method for magnetic resonance brain image classification based on adaptive chaotic pso. Progress In Electromagnetics Research, 109, 325–343. doi:10.2528/pier10090105
  8. Ahmed Kharrat,Karim Gasmi,.Mohamed Abid. 2010. Automated classification of magnetic resonance brain images using Wavelet Genetic Algorithm and SVM. Proc. 9th IEEE Int. Conf. on Cognitive Informatics (ICCI’10)978-1-4244-8040-1/10/$26.00 ©2010 IEEE
  9. Kailash D.Kharat & Pradyumna P.Kulkarni & M.B.Nagori. 2012. Brain Tumor Classification Using Neural Network Based Methods,International Journal of Computer Science and Informatics ISSN (PRINT): 2231 –5292, Vol-1, Iss-4, 2012
  10. Rashid, M. H. O., Mamun, M. A., Hossain, M. A., & Uddin, M. P. 2018. Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). doi:10.1109/ic4me2.2018.8465613
  11. Shweta Jain,Brain. 2013. Brain cancer classification using GLCM based feature extraction in artificial neural network,International Journal of Computer Science & Engineering Technology (IJCSET),ISSN : 2229-3345,Vol. 4 No. 07 Jul 2013
  12. Mohd Fauzi Bin Othman, Noramalina Bt Abdullah, Nurul Fazrena Bt K. 2011. ,Brain tumor Classification using Support Vector Machine,978-1-4577-0005-7/11/$26.00 ©2011 IEEE
  13. Sridhar, D., & Murali Krishna, I. V. 2013. Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network. 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition. doi:10.1109/icsipr.2013.6497966
  14. Pauline John. 2012. Brain tumor classification using wavelet and texture based neural network, International Journal of Scientific & Engineering Research Volume 3, Issue 10, October-2012, ISSN 2229-5518
  15. Othman, M. F., & Basri, M. A. M. 2011. Probabilistic Neural Network for Brain Tumor Classification. 2011 Second International Conference on Intelligent Systems, Modelling and Simulation. doi:10.1109/isms.2011.32
  16. Lahmiri, S., & Boukadoum, M. 2011. Classification of brain MRI using the LH and HL wavelet transform sub-bands. 2011 IEEE International Symposium of Circuits and Systems (ISCAS). doi:10.1109/iscas.2011.5937743
  17. Arizmendi, C., Sierra, D. A., Vellido, A., & Romero, E. 2011. Brain tumour classification using Gaussian decomposition and neural networks. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6091366
  18. Evangelia I. Zacharaki,Sumei Wang,Sanjeev. 2009.Classification of Brain Tumor Type and Grade Using MRI Texture and Shape in a Machine Learning Scheme,https://doi.org/10.1002/mrm.22147
  19. Ahmmed, R., Swakshar, A. S., Hossain, M. F., & Rafiq, M. A. 2017. Classification of tumors and it stages in brain MRI using support vector machine and artificial neural network. 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). doi:10.1109/ecace.2017.7912909
  20. Wasule, V., & Sonar, P. 2017. Classification of brain MRI using SVM and KNN classifier. 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). doi:10.1109/ssps.2017.8071594
  21. Nitish Zulpe and Vrushsen Pawar. 2012. GLCM Textural Features for Brain Tumor Classification,IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, May 2012 ISSN (Online): 1694-0814
  22. Kharrat, A., Halima, M. B., & Ben Ayed, M. 2015. MRI brain tumor classification using Support Vector Machines and meta-heuristic method. 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). doi:10.1109/isda.2015.7489271
  23. Machhale, K., Nandpuru, H. B., Kapur, V., & Kosta, L. 2015. MRI brain cancer classification using hybrid classifier (SVM-KNN). 2015 International Conference on Industrial Instrumentation and Control (ICIC). doi:10.1109/iic.2015.7150592
  24. Minz, A., & Mahobiya, C. 2017. MR Image Classification Using Adaboost for Brain Tumor Type. 2017 IEEE 7th International Advance Computing Conference (IACC). doi:10.1109/iacc.2017.0146
  25. Zacharaki, E. I., Sumei Wang, Chawla, S., Dong Soo Yoo, Wolf, R., Melhem, E. R., & Davatzikos, C. 2009. MRI-based classification of brain tumor type and grade using SVM-RFE. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. doi:10.1109/isbi.2009.5193232
  26. Chowdhury, Avirup; Das, Indrajit; Avipsa Roy Chowdhury; Halder, Arnab. 2018. detection and classification of brain tumor using ML, DOI:10.26483/ijarcs.v9i2.5807
  27. QURAT-UL-AIN, GHAZANFAR LATIF, SIDRA BATOOL KAZMI, M. ARFAN JAFFAR, ANWAR M. MIRZA. 2010. Classification and segmentation of brain tumor using texture analysis, RECENT ADVANCES in ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING and DATA BASES,ISSN: 1790-5109, ISBN: 978-960-474-154-0
  28. Mukambika P. S., Uma Rani K. 2017. Segmentation and Classification of MRI Brain Tumor,International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 04 Issue: 07 | July -2017
  29. Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., & Ahuja, C. K. 2013.. Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification. Journal of Digital Imaging, 26(6), 1141–1150. doi:10.1007/s10278-013-9600-0
  30. N. Abdulla, U.K. Nagh,S.Z.Aziz. 2011. Image Classificationof Brain MRI Using Support Vector Machine. DOI: 10:1109/IST.2011.5962185
  31. Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. 2018. Multi-Grade Brain Tumor Classification using Deep CNN with Extensive Data Augmentation. Journal of Computational Science. doi:10.1016/j.jocs.2018.12.003
  32. V.Vani, M.K. Geetha. 2016. Automatic Tumor Classification of Brain MRI Images using DWT Features,Volume 7, No. 5, September-October 2016, International Journal of Advanced Research in Computer Science
  33. Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., … Feng, Q. 2015. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLOS ONE, 10(10), e0140381. doi:10.1371/journal.pone.0140381
  34. Zia, R., Akhtar, P., & Aziz, A. 2017. A new rectangular window based image cropping method for generalization of brain neoplasm classification systems. International Journal of Imaging Systems and Technology, 28(3), 153–162. doi:10.1002/ima.22266
  35. Herrera, L. J., Rojas, I., Pomares, H., Guillen, A., Valenzuela, O., & Banos, O. 2013. Classification of MRI Images for Alzheimer’s Disease Detection. 2013 International Conference on Social Computing. doi:10.1109/socialcom.2013.127
  36. Sudipta Roy , Sanjay Nag , Indra Kanta Maitra , Prof. Samir Kumar Bandyopadhyay. 2013. A Review on Automated Brain Tumor Detection and Segmentation from Brain MRI, arXiv:1312.6150 cs.CV]
  37. Chandra, G. R., & Rao, K. R. H. 2016. Tumor Detection In Brain Using Genetic Algorithm. Procedia Computer Science, 79, 449–457. doi:10.1016/j.procs.2016.03.058
  38. Mohan, G., & Subashini, M. M. 2018. MRI based medical image analysis: Survey on brain tumor grade classification. Biomedical Signal Processing and Control, 39, 139–161. doi:10.1016/j.bspc.2017.07.007
  39. Raouia Ayachi and Nahla Ben Amor. 2009. Brain tumor segmentation using SVM, C. Sossai and G. Chemello (Eds.): ECSQARU 2009, LNAI 5590, pp. 736–747, 2009.c Springer-Verlag Berlin Heidelberg 2009
  40. A. Ortiz, J.M. Gorriz, J. Ramirez, D. Salas-Gonzalez, J.M. Llamas-Elvira. 2013. Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies, Appl. Soft Comput. J. 13 (2013) 2668–2682, http://dx. doi.org/10.1016/j.asoc.2012.11.020.
  41. S. Rajeshwari,T. Sree Sharmila. 2013. Quality measurements of MRI using Preprocessing techniques,Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013),978-1-4673-5758-6/13/$31.00 © 2013 IEEE
  42. W. Nitz,Principles of Magnetic Resonance Imaging and Magnetic Resonance Angiography,Mosby Year Book, St Louis
  43. Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., … Kleihues, P. 2007. The 2007 WHO Classification of Tumours of the Central Nervous System. Acta Neuropathologica, 114(2), 97–109. doi:10.1007/s00401-007-0243-4
  44. Frankly-speaking-about-cancer-brain-tumors, National brain tumor society.
  45. Perry Sprawls Jr., Emory University,Magnetic-resonance-imaging-principles-methods-and-techniques,Medical Physics Publishing Corporation
  46. Loncaric, S. (1998). A survey of shape analysis techniques. Pattern Recognition, 31(8), 983–1001. doi:10.1016/s0031-2023(97)00122-2
  47. A. Materka, M. Strzelecki. 1998. Texture Analysis Methods– A Review, Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998
  48. Nikhil R. Pal and Sankar K. Pal. 1993. A review on image segmentation techniques-PR-26-9-1993- p 1277-1294
  49. Gaurav kumar and P.K. Bhatia.2014. A Detailed Review of_Feature_Extraction, 2014 Fourth International Conference on Advanced Computing & Communication Technologies,978-1-4799-4910-6/14, IEEE,DOI 10.1109/ACCT.2014.74
  50. Smitha P., Shaji L. and Mini M.G. 2011. Review on medical image classification,nternational Conference on VLSI, Communication & Instrumentation (ICVCI) 2011, Proceedings published by International Journal of Computer Applications® (IJCA)
  51. Komal Sharma, Akwinder Kaur, Shruti Gujral. 2014. A review on various brain tumor detection techniques in brain MRI images. OSR Journal of Engineering (IOSRJEN),ISSN(e):2250-3021,ISSN(p):2278-8719, Vol. 04, Issue 05(May. 2014),||V3||PP 06-12

International Journal of Scientific Research in Science and Technology

Downloads

Published

2019-12-30

Issue

Volume 6, Issue 6, November-December-2019

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bichitra Panda, Dr. Chandra Sekhar Panda "A Review on Brain Tumor Classification Methodologies" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 6, Issue 6, pp.346-359, November-December-2019. Available at doi : https://doi.org/10.32628/IJSRST20717