A Comprehensive Review on Brain Tumour Segmentation Using Deep Learning Approach

One of the most critical tasks in medical image processing is the analysis of brain tumour. Early detection of the disease however helps and assists in enhancing prior treatments and thereby increasing the survival rates of the patient. The process of conducting a manual segmentation through a series of MRI images of the brain helps to diagnose cancer. It is further accompanied through clinical routines and check-ups. The procedure of the same appears to be a tedious and time consuming job. Hence there is a need to automate the process by using advanced technologies such as machine learning and deep learning. The fundamental aim of this paper is to contribute in providing a detailed review of brain tumour segmentation methods along with the proposed method for final implementation. Automated form of brain segmentation has been widely adopted by various research scholars; wherein they use deep learning based algorithms to address the problems that remained restricted by using ML strategies. Many algorithms and techniques that fall under DL are capable to produce better results through objective evaluation on large amounts of MRI based patient data. With large and extensive research being done in the domain by using ML; this review paper differs by highlighting the trend and strategies used in DL. An introduction to brain tumours is presented in the paper followed by state-of-art algorithms which are to be proposed during the implementation phase. An assessment on current and existing work is also briefed and future directions to standardize the process of tumour segmentation are addressed.

CNN, deep learning, EfficientNetB2, MRI, image processing

1. De Angelis L M. Brain Tumors. N. Engl. J. Med. 2001; 344:114-23 [Google Scholar] [Crossref]

2. Deimling A. Gliomas. Recent Results in Cancer Research vol 171. Berlin: Springer; 2009. [Google Scholar] [Crossref]

3. Stupp R. Malignant glioma: ESMO clinical recommendations for diagnosis, treatment and follow-up. Ann Oncol 2007; 18(Suppl 2):69-70 [Google Scholar] [Crossref]

4. Drevelegas A and Papanikolou N. Imaging modalities in brain tumors Imaging of Brain Tumors with Histological Correlations. Berlin: Springer; 2011; chapter 2:13-34 [Google Scholar] [Crossref]

5. Menze B, et al. The Multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 2015; 34(10):1993-2024 [Google Scholar] [Crossref]

6. Almadhoun, H.R.; Abu-Naser, S.S. Detection of Brain Tumor Using Deep Learning. Int. J. Acad. Eng. Res. (IJAER) 2022, 6, 29–47 [Google Scholar] [Crossref]

7. Musallam, A.S.; Sherif, A.S.; Hussein, M.K. A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images. IEEE Access 2022, 10, 2775–2782 [Google Scholar] [Crossref]

8. Woźniak, M.; Siłka, J.; Wieczorek, M. Deep Neural Network Correlation Learning Mechanism for CT Brain Tumor Detection. Neural Comput. Appl. 2021, 1–16. Available online: https://link.springer.com/article/10.1007/s00521-021-05841-x [Google Scholar] [Crossref]

9. Amin, J.; Sharif, M.; Haldorai, A.; Yasmin, M.; Nayak, R.S. Brain tumor detection and classification using machine learning: A comprehensive survey. Complex Intell. Syst. 2022, 8, 3161–3183 [Google Scholar] [Crossref]

10. Nayak, D.R.; Padhy, N.; Mallick, P.K.; Zymbler, M.; Kumar, S. Brain Tumor Classification Using Dense Efficient-Net. Axioms 2022, 11, 34 [Google Scholar] [Crossref]

11. Obeidavi, M.R.; Maghooli, K. Tumor Detection in Brain MRI using Residual Convolutional Neural Networks. In Proceedings of the 2022 IEEE International Conference on Machine Vision and Image Processing (MVIP), Ahvaz, Iran, 23–24 February 2022; pp. 1–5 [Google Scholar] [Crossref]

12. Khalil, H.A.; Darwish, S.; Ibrahim, Y.M.; Hassan, O.F. 3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm. Symmetry 2020, 12, 1256. [Google Scholar] [Crossref]

13. Sajid, S.; Hussain, S.; Sarwar, A. Brain tumor detection and segmentation in MR images using deep learning. Arab. J. Sci. Eng. 2019, 44, 9249–9261 [Google Scholar] [Crossref]

14. Lotlikar, V.S.; Satpute, N.; Gupta, A. Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. Curr. Med. Imaging 2022, 18, 604–622 [Google Scholar] [Crossref]

15. Attallah, O.; Sharkas, M.A.; Gadelkarim, H. Deep learning techniques for automatic detection of embryonic neurodevelopmental disorders. Diagnostics 2020, 10, 27 [Google Scholar] [Crossref]

16. Urban G. et al. Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2014:31–35 [Google Scholar] [Crossref]

17. Zikic D. et al. Segmentation of brain tumor tissues with convolutional neural networks. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2014:36–39 [Google Scholar] [Crossref]

18. Havaei M, Davy A, Farley W D, Biard A, Courville A, Bengio Y, Pal C, Jadoin P M, Larochelle H. Brain tumor segmentation with deep neural networks. Medical Image Analysis 2016 [Google Scholar] [Crossref]

19. Davy A. et al. Brain tumor segmentation with deep neural networks. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2014:1–5 [Google Scholar] [Crossref]

20. Pereira S, Pinto A, Alves V, Silva C A. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016;35(5):1240–1251 [Google Scholar] [Crossref]

21. Dvorak P, Menze B. Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2015:13–24. [Google Scholar] [Crossref]

22. Rao V, Sarabi M S, Jaiswal A. Brain tumor segmentation with deep learning. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2015:56–59 [Google Scholar] [Crossref]

23. Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013; 31(8):1426–38 [Google Scholar] [Crossref]

24. Foo JL. A survey of user interaction and automation in medical image segmentation methods. Tech rep ISUHCI20062, Human Computer Interaction Department, Iowa State Univ; 2006 [Google Scholar] [Crossref]

25. Havaei M, Larochelle H, Poulin P, Jadoin P M. Within-brain classification for brain tumor segmentation. Int J Cars 2016; 11:777-788 [Google Scholar] [Crossref]

26. Bauer S, Wiest R, Nolte L, Reyes M. A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol.2013;58:97-129 [Google Scholar] [Crossref]

27. https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri [Google Scholar] [Crossref]

28. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556 [Google Scholar] [Crossref]

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