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A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

  • Arshia Rehman
  • Saeeda Naz
  • Muhammad Imran RazzakEmail author
  • Faiza Akram
  • Muhammad Imran
Article

Abstract

Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

Keywords

Brain tumor Deep learning Transfer learning AlexNet GoogLeNet VGG Figshare dataset 

Notes

References

  1. 1.
    T.A. Abir, J.A. Siraji, E. Ahmed, B. Khulna, Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol. 4(8), 65–79 (2018)Google Scholar
  2. 2.
    N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, T.R. Mengko, Brain tumor classification using convolutional neural network. In World Congress on Medical Physics and Biomedical Engineering, pp. 183–189. Springer (2019)Google Scholar
  3. 3.
    P. Afshar, A. Mohammadi, K.N Plataniotis, Brain tumor type classification via capsule networks. arXiv preprint: arXiv:1802.10200 (2018)
  4. 4.
    J. Cheng, Brain tumor dataset. figshare. dataset. https://doi.org/10.6084/m9.figshare.1512427.v5. Accessed 30 May 2018
  5. 5.
    J. Cheng, W. Yang, M. Huang, W. Huang, J. Jiang, Y. Zhou, R. Yang, J. Zhao, Y. Feng, Q. Feng, Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE 11(6), e0157112 (2016)CrossRefGoogle Scholar
  6. 6.
    J. Cheng, W. Huang, R. Shuangliang Cao, W.Y. Yang, Z. Yun, Z. Wang, Q. Feng, Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)CrossRefGoogle Scholar
  7. 7.
    J. Deng, W.Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition, 2009 CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  8. 8.
    M.R. Ismael, I. Abdel-Qader, Brain tumor classification via statistical features and back-propagation neural network. In 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 0252–0257. IEEE (2018)Google Scholar
  9. 9.
    A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    A. Naseer, M. Rani, S. Naz, M.I. Razzak, M. Imran, G. Xu, Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput. Appl. (2019).  https://doi.org/10.1007/s00521-019-04069-0 Google Scholar
  11. 11.
    S. Naz, A.I. Umar, R. Ahmad, I. Siddiqi, S.B. Ahmed, M.I. Razzak, F. Shafait, Urdu Nastaliq recognition using convolutional–recursive deep learning. Neurocomputing 243, 80–87 (2017)CrossRefGoogle Scholar
  12. 12.
    I. Razzak, M. Imran, G. Xu, Efficient brain tumor segmentation with multiscaleancer statistics two-pathway-group conventional neural networks. IEEE J. Biomed. Health Inf. (2018).  https://doi.org/10.1109/JBHI.2018.2874033 Google Scholar
  13. 13.
    M.I. Razzak, Malarial parasite classification using recurrent neural network. Int. J. Image Process. 9, 69 (2015)Google Scholar
  14. 14.
    M.I. Razzak, B. Alhaqbani, Automatic detection of malarial parasite using microscopic blood images. J. Med. Imaging Health Inform. 5(3), 591–598 (2015)CrossRefGoogle Scholar
  15. 15.
    M.I. Razzak, M. Imran, G. Xu, Big data analytics for preventive medicine. Neural Comput. Appl. (2019).  https://doi.org/10.1007/s00521-019-04095-y Google Scholar
  16. 16.
    M.I. Razzak, S. Naz, Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 801–807. IEEE (2017)Google Scholar
  17. 17.
    M.I. Razzak, S. Naz, A. Zaib, Deep learning for medical image processing: overview, challenges and the future. In Classification in BioApps, pp. 323–350. Springer (2018)Google Scholar
  18. 18.
    A. Rehman, S. Naz, M.I. Razzak, H.A. Ibrahim, Automatic visual features for writer identification: a deep learning approach. IEEE Access 7, 17149–17157 (2019)CrossRefGoogle Scholar
  19. 19.
    A. Rehman, S. Naz, M.I. Razzak, Writer identification using machine learning approaches: a comprehensive review. Multimed. Tools Appl. 78(8), 10889–10931 (2019)CrossRefGoogle Scholar
  20. 20.
    O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    S.H. Shirazi, A.I. Umar, S. Naz, M.I. Razzak, Efficient leukocyte segmentation and recognition in peripheral blood image. Technol. Health Care 24(3), 335–347 (2016)CrossRefGoogle Scholar
  22. 22.
    R. Siegel, C.R. Miller, A. Jamal, Cancer statistics, 2017. CA Cancer J. Clin. 67(1), 7–30 (2017)CrossRefGoogle Scholar
  23. 23.
    R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2015. CA Cancer J. Clin. 65(1), 5–29 (2015)CrossRefGoogle Scholar
  24. 24.
    K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint: arXiv:1409.1556 (2014)
  25. 25.
    C. Szegedy, W.Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  26. 26.
    W. Widhiarso, Y. Yohannes, C. Prakarsah, Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. IJEIS (Indones. J. Electron. Instrum. Syst.) 8(2), 179–190 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentAbbottabadPakistan
  2. 2.University of Technology SydneyUltimoAustralia
  3. 3.Radiology DepartmentAyub Medical and Teaching InstituteAbbottabadPakistan
  4. 4.College of Applied Computer ScienceKing Saud UniversityRiyadhSaudi Arabia

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