Brain tumor detection from MRI images using machine learning and Deep learning techniques

IPEM Journal Computer Applications > Brain tumor detection from MRI images using machine learning and Deep learning techniques

Brain tumor detection from MRI images using machine learning and Deep learning techniques

Deshagouni Manasa, Manish Pandey

Vol 7 , Issue 1 , December 2022 | Pages: 17-22

Published Online: December, 2022

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Author Details

1. Deshagouni Manasa, Department of Computer Science Engineering, Maulana Azad National Institute of Technology , Bhopal, India
2. Manish Pandey, Department of Computer Science Engineering, Maulana Azad National Institute of Technology , Bhopal, India

The growth of abnormal brain cells, some of which may turn malignant, is what causes a brain tumour. Images from magnetic resonance imaging (MRI) are frequently used to detect brain tumours. The MRI images of the brain can be used to identify unusual tissue growth. Several research publications employ deep learning and machine learning algorithms to detect brain tumours. It is simpler to treat patients when brain tumours are swiftly and accurately identified using these algorithms in combination with MRI scans. These projections assist the doctor in making decisions. The radiologist can make decisions more quickly thanks to these projections. In the proposed work, we have used Support Vector Machine (SVM) and the M2 model is applied in detecting the presence of brain tumours.

Keywords

Support Vector Machine, Convolution Neural Network, Machine Learning, Deep Learning, Brain tumour
  1. Park, J.G., Lee, C.: Skull stripping based on region growing for magnetic resonance brain images. NeuroImage 47(4), 1394–1407 (2009)
  2. Khan, M.A., Lali, I.U., Rehman, A., Ishaq, M., Sharif, M., Saba, T., Zahoor, S., Akram, T.: Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microscopy research and technique 82(6), 909–922 (2019)
  3. Raza, M., Sharif, M., Yasmin, M., Masood, S., Mohsin, S.: Brain image representation and rendering: A survey. Research Journal of Applied Sciences, Engineering and Technology 4(18), 3274–3282 (2012)
  4. Watson, C., Kirkcaldie, M., Paxinos, G.: The Brain: an Introduction to Functional Neuroanatomy. Academic Press, ??? (2010) https://en.wikipedia.org/wiki/brainsize.accessed19oct2019
  5. Dubin, M.W.: How the Brain Works. John Wiley & Sons, ??? (2013)
  6. Koziol, L.F., Budding, D.E., Chidekel, D.: From movement to thought: executive function, embodied cognition, and the cerebellum. The Cerebellum 11(2), 505–525 (2012)
  7. Knierim, J.: Neuroscience online chapter 5: Cerebellum. The University of Texas Health Science Center at Houston (1997)
  8. Kleihues, P., Burger, P.C., Scheithauer, B.W.: The new who classification of brain tumours. Brain pathology 3(3), 255–268 (1993)
  9. Nalbalwar, R., Majhi, U., Patil, R., Gonge, S.: Detection of brain tumor by using ann. image 2(3), 7 (2014)
  10. Milletari, F., Ahmadi, S.-A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B., B¨otzel, K., et al.: Hough- cnn: Deep learning for segmentation of deep brain regions in mri and ultrasound. Computer Vision and Image Understanding 164, 92–102 (2017)
  11. O¨ zyurt, F., Sert, E., Avci, E., Dogantekin, E.: Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Measurement 147, 106830 (2019)
  12. Hashemzehi, R., Mahdavi, S.J.S., Kheirabadi, M., Kamel, S.R.: Detection of brain tumors from mri images base on deep learning using hybrid model cnn and nade. biocybernetics and biomedical engineering 40(3), 1225– 1232 (2020)
  13. Tang, Z., Ahmad, S., Yap, P.-T., Shen, D.: Multi-atlas segmentation of mr tumor brain images using low-rank based image recovery. IEEE transactions on medical imaging 37(10), 2224–2235 (2018)
  14. Singh, B., Aggarwal, P.: Detection of brain tumor using modified mean- shift based fuzzy c-mean segmentation from mri images. In: 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 536–545 (2017). IEEE
  15. Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., Feng, Q.: Brain tumor segmentation based on local independent projection-based classification. IEEE transactions on biomedical engineering 61(10), 2633–2645 (2014)
  16. Huda, S., Yearwood, J., Jelinek, H.F., Hassan, M.M., Fortino, G., Buckland, M.: A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis. IEEE access 4, 9145–9154 (2016)
  17. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor- detection Noble, W.S.: What is a support vector machine? Nature biotechnology 24(12), 1565–1567 (2006)
  18. https://www.javatpoint.com/machine-learning-support-vector-machine- algorithm O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)
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