Journal Press India^®

Author Details ( * ) denotes Corresponding author

The purpose of this study is to identify the algorithms and approaches that have been used in previous research for the classification of multiclass brain tumours in Computed Tomography (CT) or Magnetic Resonance (MR) images. The criteria for this categorization are laid out by the WHO. The precise diagnosis of tumour cells has been the focus of a great deal of research attention in recent years, and numerous new ideas and pieces of information have been generated in this area. Their study's accuracy findings imply the extent to which their concepts were discovered to provide more precise outcomes when categorizing tumour types into their appropriate groups. These findings demonstrate the efficacy of their concepts in producing more precise classifications of tumour types. Over the course of this study, we focused primarily on developing supervised classification algorithms for use on 2D MRI or CT scans of different forms of brain tumours. This study provides a comprehensive evaluation of the numerous approaches taken to assign classification labels to tumour cells.

Keywords

Brain Tumor Classification; Magnetic Resonance Imaging; Computed Tomography

1. Ratan R., Sharma S., Sharma S.K., “Multiparameter segmentation and quantization of brain tumor from MR images” Indian Journal of Science and Technology Vol.2 No 2, pp. 11-15 Feb. 2009.


2. Joshi D., Rana N.K., Misra V., “Classification of Brain Cancer Using Artificial Neural Network” International Conference on Electronic Computer Technology, pp.112-116, 2010


3. Astrocytomas grading and their malignancy, http://en.wikipedia.org/wiki/Astrocytoma.


4. Borden N., Forseen S.E., "Pattern Recognition Neuroradiology", Cambridge University Press, New York, 2011.


5. Badarneh A., Najadat H., Alaraziqi A.M., “A classifier to detect Tumor Disease in MR Brain Images”, International Conference on Advances in Social Networks Analysis and Mining, IEEE 2012.


6. Arakeri M.P., Reddy G.R.M., “An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis”, International Journal of Multimedia and Information Retrieval, Springer 2013.


7. Sachdeva J., Kumar V., Gupta I., “Segmentation, Feature Extraction and Multiclass Brain Tumor Classification”, International Journal of Digital Imaging, Springer 2013.


8. Rajni N.H., Bhavani R., “Automatic classification of CT brain images using ANN, KNN and SVM”, International Journal of Artificial Intelligence & Society, Springer 2013.


9. Sridhar D., Krishna M., “Brain Tumor Classification Using Discrete Cosine Transform and Probabilistic Neural Network”, International Conference on Signal Processing, Image Processing and Pattern Recognition, IEEE 2013.


10. Nanthagopal A.P., Rajamony R.S., “Automatic classification of brain computed tomography images using wavelet-based statistical texture features”, International Journal of Computer Vision, Springer 2012.


11. Nanthagopal A.P., Rajamony R.S., “Classification of benign and malignant brain tumor CT images using wavelet texture parameters and neural network classifier International Journal of Computer Vision, Springer 2012.


12. Gupta V., Sagale K.S., “Implementation of Classification System for Brain Cancer Using Back propagation Network and MRI”, IEEE International Conference On Engineering, Nuicone-2012.


13. Rajendran P., Madheswaran M., Naganandhini K., “An Improved Pre-Processing Technique with Image Mining Approach for the Medical Image Classification”, Second International conference on Computing, Communication and Networking Technologies, 2012 8


14. Rehman Y., Azim F., “Comparison of Different artificial Neural Networks for Brain Tumor Classification via Magnetic resonance Images”, 14th International Conference on Modeling and Simulation, IEEE 2012.


15. Othman M.F., Ariffanan M., Basri M., “Probabilistic Neural Network For Brain Tumor Classification”, Second International Conference on Intelligent Systems, Modeling and Simulation 2011.


16. Zacharaki E.I., Kanas V.G., “Investigating Machine Learning Techniques for MRI based Classification of brain neoplasm”, International Journal of Computer Assisted Radiology and Surgery, Springer 2011.


17. Fauzi M., Othman B., Abdullah N.B., “MRI Brain Classification Using Support Vector Machine”, 4 th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), IEEE 2011.


18. Arizmendi C., Vellido A., Romero E., “Binary Classification of Brain Tumors Using a Discrete Wavelet Transform and Energy Criteria”, Second Latin American Symposium on Circuits and Systems (LASCAS), IEEE 2011.


19. Deepa S.N., Devi B.A., “Artificial Neural Networks design for Classification of Brain Tumor’’, International Conference on Computer Communication and Informatics, IEEE 2011.


20. Joshi D.M., Rana N.K., Misra V.M., “Classification of Brain Cancer Using Artificial Neural Network”, International Conference on Electronic Computer Technology (ICECT), IEEE 2010.


21. Zacharaki E.I., Wang S., Chawla S., Yoo D.S., “MRI-based Classification Of Brain Tumor Type And Grade Using SVM_RFE”, International Symposium on Biomedical Imaging: From Nano to Macro, IEEE 2009.


22. http://www.hopkinsmedicine.org/healthlibrary/conditions/nervous_system_disorders/brain _tumors_85,P00775/


23. http://www.nhs.uk/Conditions/Brain-tumour/Pages/Introduction.aspx


24. http://www.braintumor.org/patients-family-friends/about-brain-tumors/symptoms-anddiagnosis.html


25. http://www.cancer.gov/cancertopics/wyntk/brain/page1/AllPages


26. Tatter S.B., “The new WHO Classification of Tumors affecting the Central Nervous System”, http://neurosurgery.mgh.harvard.edu/newwhobt.htm


27. Kleihues P., Burger P.C., Scheithauer B.W., “The new WHO classification of brain tumours”, US National Library of Medicine National Institutes of Health 1993 July 3(3):255-68.


28. Lin C., Liu, “A Tutorial of the Wavelet Transform”, Google scholar article, February 23, 2010


29. http://en.wikipedia.org



 
30.