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Detection and Classification of Brain Tumor Using Magnetic Resonance Images

  • Limali Sahoo
  • Lokanath Sarangi
  • Bidyut Ranjan Dash
  • Hemanta Kumar PaloEmail author
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)

Abstract

The paper aims to provide a comparative study on the detection and classification of brain tumors (BT) using different machine learning algorithms. In the process, different popular and commonly BT image data sets such as the BRATS, OASIS, and the NBTR have been used for the said purpose. The pre-processed BT images are enhanced using the filtering approach and then segmented using the fuzzy C-means (FCM) algorithm for the extraction of suitable and reliable features. The multi-resolution capability of wavelet transform (WT) has been explored to extract the detailed coefficients for simulation of the chosen classifiers. The recognition accuracy of the classification algorithms such as the K-nearest neighbor (KNN), decision tree (DT), neural network (NN), discriminant analyzer (DA), support vector machine, and Naive Bays’ (NB) have been compared for their applicability in classifying BT images. The highest average recognition accuracy of 96.4% has been reported with the KNN algorithms for the OASIS data set as revealed from our results.

Keywords

Brain tumor Segmentation Magnetic resonance Clustering Image enhancement Classification 

References

  1. 1.
    Sawakare, S., Chaudhari, D.: Classification of brain tumor using discrete wavelet transform, principal component analysis, and probabilistic neural network. Int. J. Res. Emerg. Sci. Technol. 1(6), 2349–7610 (2014)Google Scholar
  2. 2.
    Shree, N.V., Kumar, T.N.R.: Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 5(1), 23–30 (2018)CrossRefGoogle Scholar
  3. 3.
    Jalab, H.A., Hasan, A.: Magnetic resonance imaging segmentation techniques of brain tumors: a review. Archives of Neuroscience, p. e84920 (2019, in press)Google Scholar
  4. 4.
    Mukaram, A., Murthy, C., Kurian, M.Z.: An automatic brain tumor detection, segmentation, and classification using MRI image. Int. J. Electron. Electr Comput Syst. 6(5), 54–65 (2017)Google Scholar
  5. 5.
    Naik, J., Patel, S.: Tumor detection and classification using decision tree in brain MRI. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 14(6), 87 (2014)Google Scholar
  6. 6.
    El-Dahshan, E.S.A., Mohsen, H.M., Revett, K., Salem, A.B.M.: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)CrossRefGoogle Scholar
  7. 7.
    Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.C.: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans. Neural Networks 3(5), 672–682 (1992)CrossRefGoogle Scholar
  8. 8.
    Singh, A.: Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 98–102 (2015). IEEEGoogle Scholar
  9. 9.
    Giraddi, S., Vaishnavi, S.V.: Detection of brain tumor using image classification. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 640–644, (2017). IEEEGoogle Scholar
  10. 10.
    Sandhya, G., Giri, K., Savitri, S.A.: Novel Approach for the detection of tumor in MR images of the brain and its classification via independent component analysis and kernel support vector machine. Imaging Med. 9(3), 33–44 (2017)Google Scholar
  11. 11.
    Kumar, P., Kumar, B. V.: Brain tumor MRI segmentation and classification using ensemble classifier. Int. J. Recent Technol. Eng, 8(1S4), 244–252 (2019)Google Scholar
  12. 12.
    Sharmila, A.A., Arun, D.C., Venkatesh, J., Sudarshan, S., Pranav, A.: Predicting survival of brain tumor patients using deep learning. Int. J. Innov, Technol. Exploring Eng. 8(6), 1441–1448 (2019)Google Scholar
  13. 13.
    Lehrer, S.: Glioblastoma and dementia may share a common cause. Med. Hypotheses 75, 67–68 (2010)CrossRefGoogle Scholar
  14. 14.
    Lehrer, S.: Glioma and Alzheimer’s disease. J. Alzheimer’s Dis. Rep. 2(1), 213–218 (2018)CrossRefGoogle Scholar
  15. 15.
    Hamamci, A., Unal, G.: Multimodal brain tumor segmentation using the tumor-cut method on the BraTS data set. Proc. MICCAI-BRATS, 19–23 (2012)Google Scholar
  16. 16.
    Akinyemi, R.O., Allan, L.M., Oakley, A., Kalaria, R.N.: Hippocampal neurodegenerative pathology in post-stroke dementia compared to other dementias and aging controls. Front. Neurosci. 11, 717 (2017)CrossRefGoogle Scholar
  17. 17.
    Fernandez-Banet, J., Esposito, A., Coffin, S., Horvath, I.B., Estrella, H., Schefzick, S., Roberts, P.: OASIS: web-based platform for exploring cancer multi-omics data. Nat. Methods 13(1), 9 (2016)CrossRefGoogle Scholar
  18. 18.
    Dong, Y., Xu, S.: A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process. Lett. 14(3), 193–196 (2007)CrossRefGoogle Scholar
  19. 19.
    Bhateja, V., Verma, A., Rastogi, K., Malhotra, C., Satapathy, S.C.: Performance improvement of decision median filter for suppression of salt and pepper noise. In Advances in Signal Processing and Intelligent Recognition Systems, pp. 287–297, Springer, Cham (2014)Google Scholar
  20. 20.
    Uma, K., Suhasini, K., Vijayakumar, M.: A comparative analysis of brain tumor segmentation techniques. Indian J. Sci. Technol. 9, 48 (2016)Google Scholar
  21. 21.
    Sharma, N., Mishra, M., Shrivastava, M.: Colour image segmentation techniques and issues: an approach. Int. J. Sci. Technol. Res. 1(4), 9–12 (2012)Google Scholar
  22. 22.
    Sharma, N., Aggarwal, L.M.: Automated medical image segmentation technique. J. Med. Phys. 35(1), 3–14 (2009)CrossRefGoogle Scholar
  23. 23.
    Zanaty, E.A., El-Zoghdy, S.F.: A novel approach for color image segmentation based on region growing. Int. J. Comput. Appl. 39(3), 123–139 (2017)Google Scholar
  24. 24.
    Sheshathri, V., Sukumaran, S.: A Hybrid Clustering Based Color Image Segmentation using Ant Colony and Particle Swarm Optimization Methods. Int. J. Innov. Technol. Exploring Eng. 8(7), 352–358 (2019)Google Scholar
  25. 25.
    Palo, H.K., Mohanty, M.N., Chandra, M.: Efficient feature combination techniques for emotional speech classification. Int. J. Speech Technol. 19, 135–150 (2016)CrossRefGoogle Scholar
  26. 26.
    Palo, H.K., Sagar, S.: Comparison of neural network models for speech emotion recognition. In: 2018 2nd International Conference of Data Science and Business Analytics, pp. 127–131 (2018). IEEEGoogle Scholar
  27. 27.
    Palo, H.K., Mohanty, M.N.: Wavelet-based feature combination for recognition of emotions. Ain Shams Eng. J. 9, 1799–1806 (2018)CrossRefGoogle Scholar
  28. 28.
    Palo, H.K., Chandra, M., Mohanty, M.N.: Recognition of human speech emotion using variants of mel-frequency cepstral coefficients. In: Advances in Systems, Control and Automation, pp. 491–498, Springer, Singapore (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Limali Sahoo
    • 1
  • Lokanath Sarangi
    • 2
  • Bidyut Ranjan Dash
    • 3
  • Hemanta Kumar Palo
    • 1
    Email author
  1. 1.Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to Be University)BhubaneswarIndia
  2. 2.College of EngineeringBhubaneswarIndia
  3. 3.Gandhi PolytechnicBerhampurIndia

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