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Journal of Applied Science & Engineering

Dhaka University Journal of Applied Science & Engineering

Issue: Vol. 6, No. 2, July 2021
Title: Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification
  • Yusuf Brima
    Department of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh
  • Mosaddek Hossain Kamal Tushar
    Department of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh
  • Upama Kabir
    Department of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh
  • Tariqul Islam
    National Institute of Neuroscience and Hospital, Dhaka, Bangladesh
Keywords: Brain Tumor; CAD; Convolutional Neural Network (CNN); Computer Vision; Deep Transfer Learning; Magnetic Resonance Imaging (MRI)

Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in radiology to create images of a patient’s anatomical and physiological structures. MRI is the prevalent medical imaging practice to find abnormalities in soft tissues. Traditionally they are analyzed by a radiologist to detect abnormalities in soft tissues, especially the brain. However, the process of interpreting a massive volume of a patient's MRI is laborious. Hence, Machine Learning methodologies can aid in detecting abnormalities in soft tissues with considerable accuracy. This research has curated a novel dataset and developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. This paper adapted the Deep Residual Convolutional Neural Network (ResNet-50) architecture for the experiments and discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40% on the curated dataset, 93.80% on the Harvard Whole Brain Atlas 97.05% accuracy on the School of Biomedical Engineering dataset. Our experimental results demonstrate the proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.

  1. M. Oguz and A. P. Cox, “Machine learning biopharma applications and overview of key steps for successful implementation.”
  2. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sanchez, ́ “A survey on deep learning inmedical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017
  3. Z.-P. Liang and P. C. Lauterbur, Principles of magnetic resonance imaging: a signal processing perspective. SPIE Optical Engineering Press, 2000
  4. M. Talo, U. B. Baloglu, OzalYıldırım, and U. R. Acharya, “Application of deep transfer learning for automated brain abnormality classification using mr images,” Cognitive Systems Research, vol. 54, pp. 176 – 188, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/ pii/S1389041718310933
  5. G. Mohan and M. M. Subashini, “Mri based medical image analysis: Survey on brain tumor grade classification,” Biomedical Signal Processing and Control, vol. 39, pp. 139–161, 2018
  6. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR, vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385
  7. E. I. Zacharaki, S. Wang, S. Chawla, D. Soo Yoo, R. Wolf, E. R. Mel hem, and C. Davatzikos, “Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 62, no. 6, pp. 1609–1618, 2009.
  8. D. Summers, “Harvard whole-brain atlas: www.med.harvard.edu/aanlib/home.html,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 74, no. 3, pp. 288–288, 2003. [Online]. Available: https://jnnp.bmj.com/content/74/3/288
  9. C. H. Moritz, V. M. Haughton, D. Cordes, M. Quigley, and M. E. Meyerand, “Whole-brain functional MR imaging activation from a finger tapping task examined with independent component analysis,” American Journal of Neuroradiology, vol. 21, no. 9, pp. 1629–1635, 2000.
  10. S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 7, pp. 674–693, 1989.
  11. S. Chaplot, L. Patnaik, and N. Jagannathan, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomedical signal processing and control, vol. 1, no. 1, pp. 86–92, 2006.
  12. E.-S. A. El-Dahshan, H. M. Mohsen, K. Revett, and A.-B. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm,” Expert systems with Applications, vol. 41, no. 11, pp. 5526–5545, 2014.
  13. S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1– 16, 2019
  14. J. S. Paul, A. J. Plassard, B. A. Landman, and D. Fabbri, “Deep learning for brain tumor classification,” in Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 10137. International Society for Optics and Photonics, 2017, p. 1013710.
  15. M. G. Ertosun and D. L. Rubin, “Automated grading of gliomas using deep learning in digital pathology images: A modular approach with ensemble of convolutional neural networks,” in AMIA Annual Symposium Proceedings, vol. 2015. American Medical Informatics Association, 2015, p. 1899.
  16. P. Afshar, K. N. Plataniotis, and A. Mohammadi, “Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 1368–1372.
  17. A. K. Anaraki, M. Ayati, and F. Kazemi, “Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms,” Biocybernetics and Biomedical Engineering, vol. 39, no. 1, pp. 63–74, 2019
  18. Y. Zhang, S. Wang, G. Ji, and Z. Dong, “An mr brain images classifier system via particle swarm optimization and kernel support vector machine,” The Scientific World Journal, vol. 2013, 2013.
  19. M. Saritha, K. P. Joseph, and A. T. Mathew, “Classification of mri brain images using combined wavelet entropy based spider web plots and probabilistic neural network,” Pattern Recognition Letters, vol. 34, no. 16, pp. 2151–2156, 2013.
  20. S. Wang, Y. Zhang, Z. Dong, S. Du, G. Ji, J. Yan, J. Yang, Q. Wang, C. Feng, and P. Phillips, “Feed-forward neural network optimized by hybridization of pso and abc for abnormal brain detection,” International Journal of Imaging Systems and Technology, vol. 25, no. 2, pp. 153–164, 2015.
  21. Y. Zhang, Z. Dong, S. Wang, G. Ji, and J. Yang, “Preclinical diagnosis of magnetic resonance (mr) brain images via discrete wavelet packet transform with tsallis entropy and generalized eigenvalue proximal support vector machine (gepsvm),” Entropy, vol. 17, no. 4, pp. 1795– 1813, 2015.
  22. D. Ranjan Nayak, R. Dash, and B. Majhi, “Stationary wavelet transform and adaboost with svm based pathological brain detection in mri scan ning,” CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders), vol. 16, no. 2, pp. 137– 149, 2017
  23. D. R. Nayak, R. Dash, and B. Majhi, “Brain mr image classification using two-dimensional discrete wavelet transform and adaboost with random forests,” Neurocomputing, vol. 177, pp. 188–197, 2016.
  24. A. Gudigar, U. Raghavendra, T. R. San, E. J. Ciaccio, and U. R. Acharya, “Application of multiresolution analysis for automated detection of brain abnormality using mr images: A comparative study,” Future Generation Computer Systems, vol. 90, pp. 359–367, 2019.
  25. H. H. Sultan, N. M. Salem, and W. Al-Atabany, “Multi-classification of brain tumor images using deep neural network,” IEEE Access, 2019. [26] P. Pławiak, “Novel genetic ensembles of classifiers applied to my ocardium dysfunction recognition based on ECG signals,” Swarm and evolutionary computation, vol. 39, pp. 192–208, 2018.
  26. O. YILDIRIM and U. B. BALO ̈ GLU, “Texture classificationsystem ̆ based on 2d-dost feature extraction method and ls-svm classifier,” Suleyman Demirel ̈ Universitesi Fen BilimleriEnstit ̈ us ̈ u Dergisi ̈, vol. 21, no. 2, pp. 350–356, 2017.
  27. P. Pławiak, “An estimation of the state of consumption of a positive displacement pump based on dynamic pressure or vibrations using neural networks,” Neurocomputing, vol. 144, pp. 471–483, 2014.
  28. K. Rzecki, T. Sosnicki, M. Baran, M. Nied ́ zwiecki, M. Kr ́ ol, T. Łojewski, ́ U. Acharya, O. Yildirim, and P. Pławiak, “Application of computational ̈ intelligence methods for the automated identification of paper-ink samples based on libs,” Sensors, vol. 18, no. 11, p. 3670, 2018.
  29. Y. Bengio and H. Lee, “Editorial introduction to the neural networks special issue on deep learning of representations,” Neural Networks, vol. 64, no. C, pp. 1–3, 2015.
  30. C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, and Z. Xie, “Deep learning and its applications in biomedicine,” Genomics, proteomics & bioinformatics, vol. 16, no. 1, pp. 17–32, 2018.
  31. O. Yıldırım, P. Pławiak, R.-S. Tan, and U. R. Acharya, “Arrhythmia ̈ detection using deep convolutional neural network with long duration ecg signals,” Computers in biology and medicine, vol. 102, pp. 411–420, 2018.
  32. U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, M. Adam, A. Gertych, and R. San Tan, “A deep convolutional neural network model to classify heartbeats,” Computers in biology and medicine, vol. 89, pp. 389–396, 2017.
  33. S. Kiranyaz, T. Ince, and M. Gabbouj, “Real-time patient-specific ecg classification by 1-d convolutional neural networks,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664–675, 2015.
  34. O. Yildirim, “A novel wavelet sequence based on deep bidirectional lstm ̈ network model for ecg signal classification,” Computers in biology and medicine, vol. 96, pp. 189–202, 2018.
  35. U. R. Acharya, H. Fujita, O. S. Lih, Y. Hagiwara, J. H. Tan, and M. Adam, “Automated detection of arrhythmias using different inter vals of tachycardia ecg segments with convolutional neural network,” Information sciences, vol. 405, pp. 81–90, 2017.
  36. O. Yildirim, R. San Tan, and U. R. Acharya, “An efficient compression of ecg signals using deep convolutional autoencoders,” Cognitive Systems Research, vol. 52, pp. 198–211, 2018.
  37. O. Yıldırım, U. B. Baloglu, and U. R. Acharya, “A deep convolutional ̈ neural network model for automated identification of abnormal eeg signals,” Neural Computing and Applications, pp. 1–12, 2018.
  38. Yusuf Brima, Mossadek Hossain Kamal Tushar, Upama Kabir, and Tariqul Islam, “Brain Tumor Magnetic Resonance Imaging Dataset,” 6 2021. [Online]. Available: https://doi.org/10.6084/m9. figshare.14778750.v2
  39. J. Cheng, “brain tumor dataset,” 4 2017. [Online]. Available: https://figshare.com/articles/brain tumor dataset/1512427
  40. S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans actions on knowledge and data engineering, vol. 22, no. 10, pp. 1345– 1359, 2009.
  41. M. C. Mabray and S. Cha, “Advanced mr imaging techniques in daily practice,” Neuroimaging clinics of North America, vol. 26, 15 no. 4, p. 647—666, November 2016. [Online]. Available: https: //doi.org/10.1016/j.nic.2016.06.010
  42. C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” CoRR, vol. abs/1808.01974, 2018. [Online]. Available: http://arxiv.org/abs/1808.01974
  43. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248–255.
  44. L. N. Smith, “Cyclical learning rates for training neural networks,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017, pp. 464–472.
  45. G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Wein berger, “Snapshot ensembles: Train 1, get m for free,” arXiv preprint arXiv:1704.00109, 2017.
  46. J. Howard et al., “fastai,” https://github.com/fastai/fastai, 2018.
  47. N. Ketkar, “Introduction to pytorch,” in Deep learning with python. Springer, 2017, pp. 195–208.
  48. K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle et al., “The cancer imaging archive (tcia): maintaining and operating a public information repository,” Journal of Digital Imaging, vol. 26, no. 6, pp. 1045–1057, 2013.
  49. J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, and Q. Feng, “Correction: Enhanced performance of brain tumor classification via tumor region augmentation and partition,” PloS One, vol. 10, no. 12, p. e0144479, 2015.