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An MRI-based deep learning approach for efficient classification of brain tumors

Abstract

Efficient and reliable identification and classification of brain tumors from imaging data is essential in the diagnosis and treatment of brain cancer cells. Magnetic resonance imaging (MRI) is the most commonly used imaging modality in the analysis of infected brain tissue. However, manual segmentation requires significant time to process data produced by magnetic resonance imaging. In this study, we present two fast and proficient brain tumor identification techniques based on deep convolutional neural networks (CNNs) using magnetic resonance imaging data for the effective detection and classification of different types of brain tumors. We use two publicly available datasets from Figshare and BraTS 2018, and apply conditional random fields to eliminate forged outputs, considering spatial information on fine segmentation tasks. The first proposed architecture, based on the Figshare dataset, classifies brain tumors as gliomas, meningiomas, or pituitary tumors. The second architecture differentiates between high- and low-grade gliomas (HGG and LGG, respectively). An intensity normalization method is also investigated as a pre-processing step, which proves highly effective at detection and classification of brain tumors in combination with data augmentation techniques. The Figshare and BraTS 2018 datasets included 3062 and 251 images, respectively. The experimental results demonstrate an accuracy of 97.3% and a dice similarity coefficient (DSC) 95.8% on the task of classifying brain tumor as gliomas, meningiomas, or pituitary tumors achieved by the first proposed CNN architecture, while second proposed CNN architecture achieved an accuracy of 96.5% with a DSC of 94.3% on the task of classifying glioma grades as HGG or LGG. Experimental results reveal that our proposed model attained improved performance and increased classification accuracy compared to state-of-the-art methods.

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Data availability

The data that support the findings of this study are openly available in the Figshare dataset https://doi.org/10.6084/m9.figshare.1512427.v5and the BRATS 2018 dataset https://www.med.upenn.edu/sbia/brats2018/registration.html.

References

  1. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2018) Brain tumor classication using convolutional neural network. In: IFMBE proceedings. Springer Singapore, pp 183–189. https://doi.org/10.1007/978-981-10-9035-6_33

  2. Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classication via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP), IEEE. https://doi.org/10.1109/icip.2018.8451379

  3. Agrawal P, Whitaker RT, Elhabian SY (2020) An optimal generative model for estimating multi-label probabilistic maps. IEEE Trans Med Imaging 39:2316–2326. https://doi.org/10.1109/tmi.2020.2968917

    Article  Google Scholar 

  4. Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classication and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39:63–74. https://doi.org/10.1016/j.bbe.2018.10.004

    Article  Google Scholar 

  5. Bauer S, Wiest R, Nolte L-P, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58:R97–R129

    Article  Google Scholar 

  6. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828. https://doi.org/10.1109/tpami.2013.50

    Article  Google Scholar 

  7. Brain Tumor Segmentation Challenge (2014) Ilastik for multi-modal brain tumor segmentation, Proceedings MICCAI BraTS (Brain Tumor Segmentation Challenge)

  8. Cheng (2017) Brain tumor dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1512427.v5

  9. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, Wang Z, Feng Q (2015) Correction: enhanced performance of brain tumor classication via tumor region augmentation and partition. PLoS ONE 10:e0144479. https://doi.org/10.1371/journal.pone.0144479

    Article  Google Scholar 

  10. Deepak S, Ameer PM (2019) Brain tumor classication using deep CNN features via transfer learning. Comput Biol Med 111:103345. https://doi.org/10.1016/j.compbiomed.2019.103345

    Article  Google Scholar 

  11. Dey N, Ashour AS, Beagum S, Pistola DS, Gospodinov M, Gospodinova ЕP, Tavares JM (2015) Parameter optimization for local polynomial approximation based intersection condence interval filter using genetic algorithm: an application for brain MRI image de-noising. J Imaging 1:60–84. https://doi.org/10.3390/jimaging1010060

    Article  Google Scholar 

  12. Dieleman S, Willett KW, Dambre J (2015) Rotation-invariant convolutional neural networks for galaxy morphology prediction. Mon Not R Astron Soc 450:1441–1459. https://doi.org/10.1093/mnras/stv632

    Article  Google Scholar 

  13. Dong et al (2017) Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. In: Annual Conference on Medical Image Understanding and Analysis. Springer, Cham (2017)

  14. Dvorak P, Menze B (2015) Structured prediction with convolutional neural networks for multimodal brain tumor segmentation, MICCAI-BRATS

  15. Ertosun MG, Rubin DL (2015) Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks. In: AMIA Annu Symp Proc

  16. Estienne T, Lerousseau M, Vakalopoulou M, Andres EA, Battistella E, Carre A, Chandra S, Christodoulidis S, Sahasrabudhe M, Sun R, Robert C, Talbot H, Paragios N, Deutsch E (2020) Deep learning based concurrent brain registration and tumor segmentation. Front Comput Neurosci. https://doi.org/10.3389/fncom.2020.00017

    Article  Google Scholar 

  17. Goetz M, Weber C, Binczyk F, Polanska J, Tarnawski R, Bobek-Billewicz B, Koethe U, Kleesiek J, Stieltjes B, Maier-Hein KH (2016) DALSA: domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans Med Imaging 35:184–196. https://doi.org/10.1109/tmi.2015.2463078

    Article  Google Scholar 

  18. Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA (2021) Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 11:e042660. https://doi.org/10.1136/bmjopen-2020-042660

    Article  Google Scholar 

  19. Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classication. IEEE Access 7:36266–36273. https://doi.org/10.1109/access.2019.2904145

    Article  Google Scholar 

  20. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with Deep Neural Networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004

    Article  Google Scholar 

  21. Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179–191. https://doi.org/10.1016/j.neucom.2018.05.011

    Article  Google Scholar 

  22. Hu K, Gan Q, Zhang Y, Deng S, Xiao F, Huang W, Cao C, Gao X (2019) Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access 7:92615–92629. https://doi.org/10.1109/access.2019.2927433

    Article  Google Scholar 

  23. Huang Z, Du X, Chen L, Li Y, Liu M, Chou Y, Jin L (2020) Convolutional neural network based on complex networks for brain tumor image classification with a modified activation function. IEEE Access 8:89281–89290. https://doi.org/10.1109/access.2020.2993618

    Article  Google Scholar 

  24. Isin A, Direkoglu C, Sah M (2016) Review of MRI-based Brain tumor image segmentation using deep learning methods. Proc Comput Sci 102:317–324. https://doi.org/10.1016/j.procs.2016.09.407

    Article  Google Scholar 

  25. Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78. https://doi.org/10.1016/j.media.2016.10.004

    Article  Google Scholar 

  26. Khan MA, Ashraf I, Alhaisoni M, Damasevicius R, Scherer R, Rehman A, Bukhari SAC (2020) Multimodal brain tumor classication using deep learning and robust feature selection: a machine learning application for radiologists. Diagnostics 10:565. https://doi.org/10.3390/diagnostics10080565

    Article  Google Scholar 

  27. Khotanlou H, Colliot O, Atif J, Bloch I (2009) 3D brain tumor segmentation in MRI using fuzzy classification symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160:1457–1473. https://doi.org/10.1016/j.fss.2008.11.016

    MathSciNet  Article  Google Scholar 

  28. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classication with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  29. Kumar S, Mankame DP (2020) Optimization driven Deep Convolution Neural Network for brain tumor classification. Biocybern Biomed Eng 40:1190–1204. https://doi.org/10.1016/j.bbe.2020.05.009

    Article  Google Scholar 

  30. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  31. Li J, Udupa JK, Tong Y, Wang L, Torigian DA (2021) Segmentation evaluation with sparse ground truth data: simulating true segmentations as perfect/imperfect as those generated by humans. Med Image Anal 69:101980. https://doi.org/10.1016/j.media.2021.101980

    Article  Google Scholar 

  32. Lyksborg M, Puonti O, Agn M, Larsen R (2015) An ensemble of 2D convolutional neural networks for tumor segmentation. In: Image analysis. Springer International Publishing, pp 201–211. https://doi.org/10.1007/978-3-319-19665-7_17.

  33. Maas AL, Hannun AY, Ng AY (2013) Rectier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th international conference on machine learning, vol 30. Atlanta, Georgia

  34. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H-C, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Leemput KV (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024. https://doi.org/10.1109/tmi.2014.2377694

    Article  Google Scholar 

  35. MICCAI (2014) Multi-modal brain tumor image segmentation using glistr (2014) In: Proceedings of BRATS Challenge–MICCAI

  36. Narmatha C, Eljack SM, Tuka AARM, Manimurugan S, Mustafa M (2020) A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02470-5

    Article  Google Scholar 

  37. Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19:143–150. https://doi.org/10.1109/42.836373

    Article  Google Scholar 

  38. Parisot S, Duau H, Chemouny S, Paragios N (2012) Joint tumor segmentation and dense deformable registration of brain MR images. In: Medical image computing and computer-assisted intervention MICCAI 2012. Springer Berlin Heidelberg, pp 651–658. https://doi.org/10.1007/978-3-642-33418-4_80

  39. Paul JS, Plassard AJ, Landman BA, Fabbri D (2017) Deep learning for brain tumor classication. In: Krol A, Gimi B (eds) Medical imaging 2017: biomedical applications in molecular structural, and functional imaging, SPIE. https://doi.org/10.1117/12.2254195

  40. Pavidraa R, Preethi R, Raja NSM, Tamizharasi P, Varthini BP (2020) Examination of the brain MRI slices corrupted with induced noise|a study with SGO algorithm. In: Advances in intelligent systems and computing. Springer Singapore, pp 681–690. https://doi.org/10.1007/978-981-15-5679-1_66

  41. Peng S, Dong Y, Wang W, Hu J, Dong W (2019) The affective facial recognition task: the influence of cognitive styles and exposure times. J vis Commun Image Represent 65:102674. https://doi.org/10.1016/j.jvcir.2019.102674

    Article  Google Scholar 

  42. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35:1240–1251. https://doi.org/10.1109/tmi.2016.2538465

    Article  Google Scholar 

  43. Prastawa M (2004) A brain tumor segmentation framework based on outlie detection. Med Image Anal 8:275–283

    Article  Google Scholar 

  44. Prastawa M, Bullitt E, Ho S, Gerig G (2003) Robust estimation for brain tumor segmentation. In: Lecture notes in computer science. Springer Berlin Heidelberg, pp 530–537. https://doi.org/10.1007/978-3-540-39903-2_65.

  45. Raja NSM, Fernandes SL, Dey N, Satapathy SC, Rajinikanth V (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0854-8

    Article  Google Scholar 

  46. Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images—a study with teaching learning based optimization. Pattern Recogn Lett 94:87–95. https://doi.org/10.1016/j.patrec.2017.05.028

    Article  Google Scholar 

  47. Rajinikanth V, Satapathy SC, Dey N, Vijayarajan R (2018) DWT-PCA image fusion technique to improve segmentation accuracy in brain tumor analysis. In: Lecture notes in electrical engineering. Springer Singapore, pp 453–462. https://doi.org/10.1007/978-981-10-7329-8_46

  48. Raschke F, Barrick TR, Jones TL, Yang G, Ye X, Howe FA (2019) Tissue-type mapping of gliomas. NeuroImage: Clin 21:101648. https://doi.org/10.1016/j.nicl.2018.101648

    Article  Google Scholar 

  49. Rehman A, Naz S, Razzak MI, Akram F, Imran M (2019) A deep learning-based framework for automatic brain tumors classication using transfer learning. Circuits Syst Signal Process 39:757–775. https://doi.org/10.1007/s00034-019-01246-3

    Article  Google Scholar 

  50. Satapathy SC, Rajinikanth V (2018) Jaya algorithm guided procedure to segment tumor from brain MRI. J Optim 2018:1–12. https://doi.org/10.1155/2018/3738049

    Article  Google Scholar 

  51. Sharma K, Virmani J (2017) A decision support system for classication of normal and medical renal disease using ultrasound images. Int J Ambient Comput Intell 8:52–69. https://doi.org/10.4018/ijaci.2017040104

    Article  Google Scholar 

  52. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640–651. https://doi.org/10.1109/tpami.2016.2572683

    Article  Google Scholar 

  53. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2016) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 12:183–203. https://doi.org/10.1007/s11548-016-1483-3

    Article  Google Scholar 

  54. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed 157:69–84. https://doi.org/10.1016/j.cmpb.2018.01.003

    Article  Google Scholar 

  55. Subbanna NK, Precup D, Collins DL, Arbel T (2013) Hierarchical probabilistic gabor and MRF segmentation of brain tumours in MRI volumes. In: Advanced information systems engineering. Springer Berlin Heidelberg, pp 751–758. https://doi.org/10.1007/978-3-642-40811-3_94.

  56. Subbanna N, Precup D, Arbel T (2014) Iterative multilevel MRF leveraging context and voxel information for brain tumour segmentation in MRI. In: 2014 IEEE conference on computer vision and pattern recognition, IEEE. https://doi.org/10.1109/cvpr.2014.58

  57. Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classication of brain tumor images using deep neural network. IEEE Access 7:69215–69225. https://doi.org/10.1109/access.2019.2919122

    Article  Google Scholar 

  58. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), IEEE. https://doi.org/10.1109/cvpr.2015.7298594

  59. Thaha MM, Kumar KPM, Murugan BS, Dhanasekeran S, Vijayakarthick P, Selvi AS (2019) Brain tumor segmentation using convolutional neural networks in MRI images. J Med Syst. https://doi.org/10.1007/s10916-019-1416-0

    Article  Google Scholar 

  60. Toğaçar M, Ergen B, Cömert C (2020) BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 134:109531. https://doi.org/10.1016/j.mehy.2019.109531

    Article  Google Scholar 

  61. Tustison NJ, Shrinidhi KL, Wintermark M, Durst CR, Kandel BM, Gee JC, Grossman MC, Avants BB (2014) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplied) with ANTsR. Neuroinformatics 13:209–225. https://doi.org/10.1007/s12021-014-9245-2

    Article  Google Scholar 

  62. Urban G, Bendszus M, Hamprecht F, Kleesiek J (2014) Multi-modal brain tumor segmentation using deep convolutional neural networks. Miccai-Bratss. 31–35

  63. Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37:1562–1573. https://doi.org/10.1109/tmi.2018.2791721

    Article  Google Scholar 

  64. Wang G, Zuluaga MA, Li W, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2019) DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans Pattern Anal Mach Intell 41:1559–1572. https://doi.org/10.1109/tpami.2018.2840695

    Article  Google Scholar 

  65. Wu W, Li D, Du J, Gao X, Gu W, Zhao F, Feng X, Yan H (2020) An intelligent diagnosis method of brain MRI tumor segmentation using deep convolutional neural Network and SVM algorithm. Comput Math Methods Med 2020:1–10. https://doi.org/10.1155/2020/6789306

    Article  Google Scholar 

  66. Wu Y, Hatipoglu S, Alonso-Alvarez D, Gatehouse P, Li B, Gao Y, Firmin D, Keegan J, Yang G (2021) Fast and automated segmentation for the three-directional multi-slice cine myocardial velocity mapping. Diagnostics 11:346. https://doi.org/10.3390/diagnostics11020346

    Article  Google Scholar 

  67. Xavier Glorot YB (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, PMLR. 9:249–256

  68. Yang Q, Zhang H, Xia J, Zhang X (2021) Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network. Quant Imaging Med Surg 11:300–316. https://doi.org/10.21037/qims-20-783

    Article  Google Scholar 

  69. Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Classication of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618. https://doi.org/10.1002/mrm.22147

    Article  Google Scholar 

  70. Zhang J, Jiang Z, Dong J, Hou Y, Liu B (2020) Attention gate ResU-net for automatic MRI brain tumor segmentation. IEEE Access 8:58533–58545. https://doi.org/10.1109/access.2020.2983075

    Article  Google Scholar 

  71. Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2021) Cross-modality deep feature learning for brain tumor segmentation. Pattern Recogn 110:107562. https://doi.org/10.1016/j.patcog.2020.107562

    Article  Google Scholar 

  72. Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111. https://doi.org/10.1016/j.media.2017.10.002

    Article  Google Scholar 

  73. Zhao Y-X, Zhang Y-M, Liu C-L (2020) Bag of tricks for 3D MRI brain tumor segmentation. In: Brain-lesion: glioma multiple sclerosis, stroke and traumatic brain injuries. Springer International Publishing, pp 210–220. https://doi.org/10.1007/978-3-030-46640-4_20.

  74. Zhou X, Li X, Hu K, Zhang Y, Chen Z, Gao X (2021) ERV-Net: an efficient 3D residual neural network for brain tumor segmentation. Expert Syst Appl 170:114566. https://doi.org/10.1016/j.eswa.2021.114566

    Article  Google Scholar 

  75. Zikic (2014) Brain tumor segmentation with deep neural networks. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp 31–35

  76. Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. In: Conference: MICCAI workshop on multimodal brain tumor segmentation challenge (BRATS), Boston, Massachusetts

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Acknowledgements

This work was supported in part by Shenzhen Science and Technology Project (No. JCYJ20200821152629001)

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Correspondence to Huang Jianjun.

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Haq, E.U., Jianjun, H., Li, K. et al. An MRI-based deep learning approach for efficient classification of brain tumors. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03535-9

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Keywords

  • Segmentation process
  • Brain tumor
  • Deep convolutional neural network
  • Data augmentation
  • Magnetic resonance images