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Comparative Study for Brain Tumor Classification on MR/CT Images

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Proceedings of the Third International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 258))

Abstract

The objective of this study is to find out the algorithms and approaches that have been used for the classification of multiclass brain tumors as specified by the World Health Organization (WHO) in Computed Tomography (CT) or Magnetic Resonance (MR) images. From the past several years lot of researchers focused in current domain and came up with new ideas and facts for proper diagnosis of the tumor cells. The accuracy results of their study give the implication of how well their ideas was found to give more accurate results of classifying tumors type into their correct classes. In this whole study, focus was made on supervised classification approaches on 2D MRI or CT images of multi class brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with correct class labels.

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References

  1. http://www.hopkinsmedicine.org/healthlibrary/conditions/nervous_system_disorders/brain_tumors_85,P00775/

  2. Ratan, R., Sharma, S., Sharma, S.K.: Multiparameter segmentation and quantization of brain tumor from MR images. Indian J. Sci. Technol. 2(2), 11–15 (2009)

    Google Scholar 

  3. Joshi, D., Rana, N.K., Misra, V.: Classification of brain cancer using artificial neural network. In: International Conference on Electronic Computer Technology, pp. 112–116. (2010)

    Google Scholar 

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

  5. Tatter, S.B.: The new WHO classification of tumors affecting the central nervous system. http://neurosurgery.mgh.harvard.edu/newwhobt.htm

  6. Kleihues, P., Burger, P.C., Scheithauer, B.W.: The new WHO classification of brain tumours. Brain Pathol. (US National Library of Medicine National Institutes of Health) 3(3):255–268 (1993)

    Google Scholar 

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

  8. Zacharaki, E.I., Wang, S., Chawla, S., Yoo, D.S.: MRI-based classification of brain tumor type and grade using SVM_RFE. In: IEEE International Symposium on Biomedical Imaging: from Nano to Macro (2009)

    Google Scholar 

  9. Borden, N., Forseen, S.E.: Pattern Recognition Neuroradiology. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  10. http://www.braintumor.org/patients-family-friends/about-brain-tumors/symptoms-and-diagnosis.html

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

  12. Badarneh, A., Najadat, H., Alaraziqi, A.M.: A classifier to detect tumor disease in MR brain images. In: IEEE International Conference on Advances in Social Networks Analysis and Mining (2012)

    Google Scholar 

  13. Arakeri, M.P., Reddy, G.R.M.: An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis. Int. J. Multimedia Inf. Retrieval (2013) (Springer)

    Google Scholar 

  14. Lin, C., Liu.: A Tutorial of the Wavelet Transform, Google scholar article, 23 Feb (2010)

    Google Scholar 

  15. http://en.wikipedia.org

  16. Sachdeva, J., Kumar, V., Gupta, I.: Segmentation, feature extraction and multiclass brain tumor classification. Int. J. Digit. Imaging 26, 1141–1150 (2013) (Springer)

    Google Scholar 

  17. Rajni, N.H., Bhavani, R.: Automatic classification of CT brain images using ANN, KNN and SVM. Int. J. Artif Intell.Soc. (2013) (Springer)

    Google Scholar 

  18. Gupta, V., Sagale, K.S.: Implementation of classification system for brain cancer using back propagation network and MRI. In: IEEE International Conference on Engineering (Nuicone), (2012)

    Google Scholar 

  19. Arizmendi, C., Vellido, A., Romero E.: Binary classification of brain tumors using a discrete wavelet transform and energy criteria. In: IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), (2011)

    Google Scholar 

  20. Nanthagopal, A.P., Rajamony, R.S.: Classification of benign and malignant brain tumor CT images using wavelet texture parameters and neural network classifier. Int. J. Comput. Vision, (2012) (Springer)

    Google Scholar 

  21. Othman, M.F., Ariffanan, M., Basri M.: Probabilistic neural network for brain tumor classification. In: Second International Conference on Intelligent Systems, Modeling and Simulation, (2011)

    Google Scholar 

  22. Zacharak,i E.I., Kanas, V.G.: Investigating machine learning techniques for MRI based classification of brain neoplasm. Int. J. Comput. Assist. Radiol. Surg. 6(6):821–828 (2011) (Springer)

    Google Scholar 

  23. Fauzi, M., Othman, B., Abdullah, N.B.: MRI brain classification using support vector machine. In: IEEE 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), (2011)

    Google Scholar 

  24. Sridhar, D., Krishna M.: Brain tumor classification using discrete cosine transform and probabilistic neural network. In: IEEE International Conference on Signal Processing, Image Processing and Pattern Recognition, (2013)

    Google Scholar 

  25. Nanthagopal, A.P., Rajamony, R.S.: Automatic classification of brain computed tomography images using wavelet-based statistical texture features. Int. J. Comput. Vision, (2012) (Springer)

    Google Scholar 

  26. Rehman, Y., Azim, F.: Comparison of different artificial neural networks for brain tumor classification via magnetic resonance images. In: IEEE 14th International Conference on Modeling and Simulation, (2012)

    Google Scholar 

  27. Rajendran, P., Madheswaran, M., Naganandhini K.: An improved pre-processing technique with image mining approach for the medical image classification. In: Second International Conference on Computing, Communication and Networking Technologies, (2012)

    Google Scholar 

  28. Deepa, S.N., Devi, B.A.: Artificial neural networks design for classification of brain tumor. In: IEEE International Conference on Computer Communication and Informatics, (2011)

    Google Scholar 

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Authors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, India

    Ankit Vidyarthi & Namita Mittal

Authors
  1. Ankit Vidyarthi
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  2. Namita Mittal
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Corresponding author

Correspondence to Ankit Vidyarthi .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  4. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Vidyarthi, A., Mittal, N. (2014). Comparative Study for Brain Tumor Classification on MR/CT Images. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_77

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  • DOI: https://doi.org/10.1007/978-81-322-1771-8_77

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1770-1

  • Online ISBN: 978-81-322-1771-8

  • eBook Packages: EngineeringEngineering (R0)

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