Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Alizadeh Savareh, Behrouz; Emami, Hassan; Hajiabadi, Mohamadreza; Ghafoori, Mahyar; Majid Azimi, Seyed

doi:10.2478/pjmpe-2018-0007

Abstract

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

References

[1] Priyanka, et al. A review on brain tumor detection using segmentation. International Journal of Computer Science and Mobile Computing. 2013;2(7):48-54.
Search in Google Scholar Back to article
[2] Maiti I, Chakraborty M. A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. in Computing and Communication Systems (NCCCS), 2012 National Conference on. 2012.10.1109/NCCCS.2012.6413020
Search in Google Scholar Back to article
[3] Joshi A, et al. An Efficient Tumor Extraction Algorithm using Segmentation of Multimodal Medical Images.
Search in Google Scholar Back to article
[4] Yu Z, et al. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg. 2016;11(11):2007-2019.10.1007/s11548-015-1330-y26914530
Search in Google Scholar Back to article
[5] Varsha YS, Shyry SP. A Novel Approach for Identifying the Stages of Brain Tumor. International Journal of Computer Trends and Technology (IJCTT) 2014. 10.10.14445/22312803/IJCTT-V10P116
Search in Google Scholar Back to article
[6] Jazayeri SB, et al. Epidemiology of Primary CNS Tumors in Iran: A Systematic. Asian Pacific Journal of Cancer Prevention. 2013;14(6):3979-3985.10.7314/APJCP.2013.14.6.3979
Open DOI Search in Google Scholar Back to article
[7] Ferlay J, et al. World cancer statistics for the most common cancers 2012. 2012 [cited 2015 16/01/2015.]; Available from: http://www.wcrf.org/int/cancer-facts-figures/worldwide-data.
Search in Google Scholar Back to article
[8] Gavrilovic IT, Posner JB. Brain metastases: epidemiology and pathophysiology. Journal of neuro-oncology. 2005;75(1):5-14.10.1007/s11060-004-8093-616215811
Search in Google Scholar Back to article
[9] Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int. Arab J. Inf. Technol. 2015;12(1): 42-52.
Search in Google Scholar Back to article
[10] Mehndiratta A, et al. An Introduction to Brain Tumor Imaging, in Tumors of the Central Nervous System, Volume 11. 2014, Springer. p. 3-20.10.1007/978-94-007-7037-9_1
Search in Google Scholar Back to article
[11] El-Melegy MT, Mokhtar HM. Tumor segmentation in brain MRI using a fuzzy approach with class center priors. EURASIP Journal on Image and Video Processing, 2014. 2014(1):1-14.10.1186/1687-5281-2014-21
Search in Google Scholar Back to article
[12] Amsaveni V, Singh NA. Detection of brain tumor using neural network. in Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on. 2013.10.1109/ICCCNT.2013.6726524
Search in Google Scholar Back to article
[13] Schmidt M, et al. Segmenting brain tumors using alignment-based features. in Machine Learning and Applications, 2005. Proceedings. Fourth International Conference on. 2005.
Search in Google Scholar Back to article
[14] Prajapati SJ, Jadhav KR. Brain tumor detection by various image segmentation techniques with introduction to non negative matrix factorization. Brain. 2015;4(3):600-603.10.17148/IJARCCE.2015.43144
Search in Google Scholar Back to article
[15] Al-Ashwal RH, et al. Digital Processing for Computed Tomography Images: Brain Tumor Extraction and Histogram Analysis. in Math Comput Contemp Sci. 14th International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (MMACTEE13). 2012.
Search in Google Scholar Back to article
[16] M Prastawa, et al. A brain tumor segmentation framework based on outlier detection. Med Image Anal. 2004;8(3):275-283.10.1016/j.media.2004.06.00715450222
Open DOI Search in Google Scholar Back to article
[17] Ding Z, Wang C, Wang P. Giant Benthic HD Image Feature Extraction and Size Estimation Based on Canny Algorithm. in Proceedings of the 2015 Chinese Intelligent Automation Conference. 2015. Springer.10.1007/978-3-662-46469-4_13
Search in Google Scholar Back to article
[18] Wang Y, Ma S. Automatic detection and segmentation of brain tumor using fuzzy classification and deformable models. in Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on. 2011.10.1109/BMEI.2011.6098610
Search in Google Scholar Back to article
[19] Heiss WD, et al. Multimodality Assessment of Brain Tumors and Tumor Recurrence. J Nucl Med. 2011;52(10):1585-1600.10.2967/jnumed.110.08421021840931
Search in Google Scholar Back to article
[20] Moon N, et al. Automatic Brain and Tumor Segmentation. MICCAI proceedings, 2002: p. 372-379.10.1007/3-540-45786-0_46
Search in Google Scholar Back to article
[21] Wang G, Wang D. Segmentation of Brain MRI Image with GVF Snake Model. in Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on. 2010.
Search in Google Scholar Back to article
[22] Bara S, et al. A robust approach for the detection of brain tumors by variational b-spline level-set method and brain extraction. in Multimedia Computing and Systems (ICMCS), 2014 International Conference on. 2014.10.1109/ICMCS.2014.6911406
Search in Google Scholar Back to article
[23] Logeswari T, Karnan M. An improved implementation of brain tumor detection using segmentation based on soft computing. Journal of Cancer Research and Experimental Oncology. 2009;2(1)::006-014.
Search in Google Scholar Back to article
[24] Toga AW, et al. Probabilistic approaches for atlasing normal and disease-specific brain variability. Anatomy and embryology. 2001; 204(4):267-282.10.1007/s00429010019811720233
Search in Google Scholar Back to article
[25] Shanthi U, et al. An Automated Computer Aided System for Tumor Detection in Brain. International Journal of Innovative Research in Computer and Communication Engineering. 2015;3(2):84-88.
Search in Google Scholar Back to article
[26] Kumar M, Mehta KK. A Texture based Tumor detection and automatic Segmentation using Seeded Region Growing Method. ijta, 2015;2(4):855-859.
Search in Google Scholar Back to article
[27] Fazli S, Nadirkhanlou P. A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images. arxiv, 2013.
Search in Google Scholar Back to article
[28] Al-Tamimi MSH, Sulong G. Tumor brain detection through MR images: a review of literature. Journal of Theoretical and Applied Information Technology. 2014;6(2):387-403.
Search in Google Scholar Back to article
[29] Malviya AM, Joshi AS. Gabor Wavelet Approach for Automatic Brain Tumor Detection. International Journal of Emerging Technology and Advanced Engineering. 2014;4(4): 826-831.
Search in Google Scholar Back to article
[30] Goswami S, Bhaiya LKP. A hybrid neuro-fuzzy approach for brain abnormality detection using GLCM based feature extraction. in Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on. 2013.10.1109/C2SPCA.2013.6749454
Search in Google Scholar Back to article
[31] Ibrahim WH, Osman AAA, Mohamed YI. MRI brain image classification using neural networks. in Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on. 2013.10.1109/ICCEEE.2013.6633943
Search in Google Scholar Back to article
[32] Phooi Yee L, Voon FCT, Ozawa S. The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks. in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. 2005.10.1109/IEMBS.2005.161562517281395
Search in Google Scholar Back to article
[33] Havaei M, et al. Brain tumor segmentation with deep neural networks. Medical image analysis. 2017;35:18-31.10.1016/j.media.2016.05.00427310171
Search in Google Scholar Back to article
[34] Sachdeva J, et al. A novel content-based active contour model for brain tumor segmentation. Magnetic resonance imaging. 2012;30(5):694-715.10.1016/j.mri.2012.01.00622459443
Search in Google Scholar Back to article
[35] Aswathy SU, Dhas GGD, Kumar SS. A survey on detection of brain tumor from MRI brain images. in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). 2014.10.1109/ICCICCT.2014.6993081
Search in Google Scholar Back to article
[36] Xuan X, Liao Q. Statistical structure analysis in MRI brain tumor segmentation. in Image and Graphics, 2007. ICIG 2007. Fourth International Conference on. 2007. IEEE.10.1109/ICIG.2007.181
Search in Google Scholar Back to article
[37] Chen D. Interactive Brain Tumor Segmentation with Inclusion Constraints. 2016, The University of Western Ontario.
Search in Google Scholar Back to article
[38] Upadhyay N, Waldman A. Conventional MRI evaluation of gliomas. Br J Radiol. 2011;84(Spec No 2):S107-11.10.1259/bjr/65711810347389422433821
Open DOI Search in Google Scholar Back to article
[39] Sheikh A, Krishna R, Dutt S. Energy Efficient Approach for Segmentation of Brain tumor Using Ant Colony Optimizationǁ. International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume. 1.
Search in Google Scholar Back to article
[40] Dass R, Devi S. Image segmentation techniques 1. 2012.
Search in Google Scholar Back to article
[41] Kumar D. Review on different Techniques of Image Segmentation using MATLAB. 2017.
Search in Google Scholar Back to article
[42] Chin-Wei B, Rajeswari M. Multiobjective Optimization Approaches in Image Segmentation–The Directions and Challenges. Int. J. Advance. Soft Comput. Appl, 2010. 2(1).
Search in Google Scholar Back to article
[43] Arbeláez PA, Cohen LD. Energy partitions and image segmentation. Journal of Mathematical Imaging and Vision. 2004;20(1):43-57.10.1023/B:JMIV.0000011318.77653.44
Open DOI Search in Google Scholar Back to article
[44] Costin H. Recent trends in medical image processing. Computer Science. 2014;22(2):65.
Search in Google Scholar Back to article
[45] Withey D, Koles Z. A review of medical image segmentation: methods and available software. International Journal of Bioelectromagnetism. 2008;10(3):125-148.
Search in Google Scholar Back to article
[46] Sachin N, et al. Brain Tumor Detection Based On Bilateral Symmetry Information. Int. Journal of Engineering Research and Application. 2013;3(6):430-432.
Search in Google Scholar Back to article
[47] Constantin AA, Bajcsy BR, Nelson CS. Unsupervised segmentation of brain tissue in multivariate MRI. in Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. 2010.
Search in Google Scholar Back to article
[48] Kobashi S, et al. Adaptive Brain Tissue Classification with Fuzzy Spatial Modeling in 3T IR-FSPGR MR Images. in Automation Congress, 2006. WAC '06. World. 2006.10.1109/WAC.2006.375748
Search in Google Scholar Back to article
[49] Yu CP, et al. 3D blob based brain tumor detection and segmentation in MR images. in Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. 2014.10.1109/ISBI.2014.6868089
Search in Google Scholar Back to article
[50] D Jude hemanth, Selvathi D, Anitha J. Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation. IEEE International Advance Computing Conference. 2009;6(7):609-614.10.1109/IADCC.2009.4809081
Search in Google Scholar Back to article
[51] Karnan M, Selvanayaki K. Improved implementation of brain MR image segmentation using Meta heuristic algorithms. in Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on. 2010.10.1109/ICCIC.2010.5705892
Search in Google Scholar Back to article
[52] Idrissi N, Ajmi FE. A hybrid segmentation approach for brain tumor extraction and detection. in Multimedia Computing and Systems (ICMCS), 2014 International Conference on. 2014.10.1109/ICMCS.2014.6911131
Search in Google Scholar Back to article
[53] Charutha S, Jayashree MJ. An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. in Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on. 2014.10.1109/ICCICCT.2014.6993142
Search in Google Scholar Back to article
[54] Vijay J, Subhashini J. An efficient brain tumor detection methodology using K-means clustering algoriftnn. in Communications and Signal Processing (ICCSP), 2013 International Conference on. 2013.10.1109/iccsp.2013.6577136
Search in Google Scholar Back to article
[55] Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. International Journal of Image, Graphics and Signal Processing. 2012;4(10):34.10.5815/ijigsp.2012.10.05
Search in Google Scholar Back to article
[56] Cuadra MB, et al. Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE transactions on medical imaging. 2004;23(10):1301-1314.10.1109/TMI.2004.83461815493697
Search in Google Scholar Back to article
[57] Shen S, et al. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE transactions on information technology in biomedicine. 2005;9(3):459-467.10.1109/TITB.2005.847500
Search in Google Scholar Back to article
[58] Corso JJ, et al. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE transactions on medical imaging. 2008;27(5):629-640.10.1109/TMI.2007.91281718450536
Search in Google Scholar Back to article
[59] Hall LO, et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE transactions on neural networks. 1992;3(5):672-682.10.1109/72.15905718276467
Search in Google Scholar Back to article
[60] Bauer S, Nolte L-P, Reyes M. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. Medical image computing and computer-assisted intervention–MICCAI 2011, 2011: p. 354-361.10.1007/978-3-642-23626-6_4422003719
Search in Google Scholar Back to article
[61] Aslan Ö, et al. Convex two-layer modeling. in Advances in Neural Information Processing Systems. 2013.
Search in Google Scholar Back to article
[62] Cho Y, Saul LK. Kernel methods for deep learning. in Advances in neural information processing systems. 2009.
Search in Google Scholar Back to article
[63] Deng L, et al. Use of kernel deep convex networks and end-to-end learning for spoken language understanding. in Spoken Language Technology Workshop (SLT), 2012 IEEE. 2012. IEEE.10.1109/SLT.2012.6424224
Search in Google Scholar Back to article
[64] Vinyals O, et al. Learning with recursive perceptual representations. in Advances in Neural Information Processing Systems. 2012.
Search in Google Scholar Back to article
[65] Huertas-Company M, et al. A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning. arXiv preprint arXiv:1509.05429, 2015.10.1088/0067-0049/221/1/8
Search in Google Scholar Back to article
[66] Bauer S, et al. Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and nonrigid registration. in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. 2010. IEEE.10.1109/IEMBS.2010.562730221096622
Search in Google Scholar Back to article
[67] Pereira S, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging. 2016;35(5):1240-1251.10.1109/TMI.2016.253846526960222
Search in Google Scholar Back to article
[68] Baker JM, et al. Developments and directions in speech recognition and understanding, Part 1 [DSP Education]. IEEE Signal Processing Magazine. 2009;26(3):75-80.10.1109/MSP.2009.932166
Search in Google Scholar Back to article
[69] Baker J. et al. Updated MINDS report on speech recognition and understanding, Part 2. 2009. Institute of Electrical and Electronics Engineers.10.1109/MSP.2009.932707
Search in Google Scholar Back to article
[70] Deng L. Computational models for speech production, in Computational models of speech pattern processing. 1999, Springer. p. 199-213.10.1007/978-3-642-60087-6_20
Search in Google Scholar Back to article
[71] Deng L. Switching dynamic system models for speech articulation and acoustics, in Mathematical Foundations of Speech and Language Processing. 2004, Springer. p. 115-133.10.1007/978-1-4419-9017-4_6
Search in Google Scholar Back to article
[72] George, D., How the brain might work: A hierarchical and temporal model for learning and recognition. 2008, Citeseer.
Search in Google Scholar Back to article
[73] Bouvrie JV. Hierarchical learning: Theory with applications in speech and vision. 2009, Citeseer.
Search in Google Scholar Back to article
[74] Poggio T. How the brain might work: The role of information and learning in understanding and replicating intelligence. Information: Science and Technology for the New Century, 2007: 45-61.
Search in Google Scholar Back to article
[75] Deng L, Yu D. Deep Learning. Signal Processing. 2014;7:3-4.10.1561/2000000039
Search in Google Scholar Back to article
[76] Hinton GE, Osindero S, The Y-W. A fast learning algorithm for deep belief nets. Neural computation. 2006;18(7):1527-1554.10.1162/neco.2006.18.7.152716764513
Search in Google Scholar Back to article
[77] Mohamed A-r, Hinton GE. Phone recognition using Restricted Boltzmann Machines. in ICASSP. 2010.10.1109/ICASSP.2010.5495651
Search in Google Scholar Back to article
[78] Le QV, et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. 2011. IEEE.10.1109/CVPR.2011.5995496
Search in Google Scholar Back to article
[79] Vincent P, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research. 2010;11: 3371-3408.
Search in Google Scholar Back to article
[80] Zhou X, et al. Product Image Search with Deep Attribute Mining and Re-ranking. in Pacific Rim Conference on Multimedia. 2016. Springer.10.1007/978-3-319-48896-7_55
Search in Google Scholar Back to article
[81] Bengio Y. Learning deep architectures for AI. Foundations and trends® in Machine Learning. 2009;2(1):1-127.10.1561/2200000006
Search in Google Scholar Back to article
[82] Ciresan D, et al. Deep neural networks segment neuronal membranes in electron microscopy images. in Advances in neural information processing systems. 2012.
Search in Google Scholar Back to article
[83] Vo DM, Le TH. Deep generic features and SVM for facial expression recognition. in Information and Computer Science (NICS), 2016 3rd National Foundation for Science and Technology Development Conference on. 2016. IEEE.10.1109/NICS.2016.7725672
Search in Google Scholar Back to article
[84] LeCun Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11):2278-2324.10.1109/5.726791
Open DOI Search in Google Scholar Back to article
[85] Huang FJ, LeCun Y. Large-scale learning with svm and convolutional for generic object categorization. in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. 2006. IEEE.
Search in Google Scholar Back to article
[86] Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016.
Search in Google Scholar Back to article
[87] Waibel A, et al. Phoneme recognition using time-delay neural networks. Acoustics, Speech and Signal Processing, IEEE Transactions on, 1989:37(3): 328-339.10.1109/29.21701
Search in Google Scholar Back to article
[88] Davy A, et al. Brain tumor segmentation with deep neural networks. 2014.
Search in Google Scholar Back to article
[89] Zikic D, et al. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS, 2014:36-39.
Search in Google Scholar Back to article
[90] Agn M, et al. Brain tumor segmentation by a generative model with a prior on tumor shape. Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, 2015: 1-4.
Search in Google Scholar Back to article
[91] Dvorak P. Menze B. Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, 2015:13-24.
Search in Google Scholar Back to article
[92] Rao V. Shari Sarabi M, Jaiswal. Brain tumor segmentation with deep learning. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), 2015: 56-59.
Search in Google Scholar Back to article
[93] Lyksborg M, et al. An ensemble of 2D convolutional neural networks for tumor segmentation. in Scandinavian Conference on Image Analysis. 2015. Springer.10.1007/978-3-319-19665-7_17
Search in Google Scholar Back to article
[94] Rewari R. Automatic Tumor Segmentation from MRI scans. Stanford University.
Search in Google Scholar Back to article
[95] Pan Y, et al. Brain tumor grading based on neural networks and convolutional neural networks. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015. IEEE.10.1109/EMBC.2015.731845826736358
Search in Google Scholar Back to article
[96] Zhao L, Jia K. Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis. in 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). 2015. IEEE.10.1109/IIH-MSP.2015.41
Search in Google Scholar Back to article
[97] Havaei M, et al. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2016.10.1016/j.media.2016.05.00427310171
Search in Google Scholar Back to article
[98] Ghafoorian M., et al. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. arXiv preprint arXiv:1610.04834, 2016.10.1038/s41598-017-05300-5
Search in Google Scholar Back to article
[99] Chen H, et al. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. arXiv preprint arXiv:1608.05895, 2016.
Search in Google Scholar Back to article
[100] Yi D. et al. 3-D Convolutional Neural Networks for Glioblastoma Segmentation. arXiv preprint arXiv:1611.04534, 2016.
Search in Google Scholar Back to article
[101] Chang PD. Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers, A. Crimi, et al., Editors. 2016, Springer International Publishing: Cham. p. 108-118.10.1007/978-3-319-55524-9_11
Search in Google Scholar Back to article
[102] Kamnitsas K, et al. DeepMedic on Brain Tumor Segmentation. Athens, Greece Proc. BRATS-MICCAI, 2016.10.1007/978-3-319-55524-9_14
Search in Google Scholar Back to article
[103] Casamitjana A, et al. 3D convolutional networks for brain tumor segmentation. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS), 2016:65-68.
Search in Google Scholar Back to article
[104] Zhao X, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. arXiv preprint arXiv:1702.04528, 2017.10.1016/j.media.2017.10.002
Search in Google Scholar Back to article
[105] Lun T, Hsu W. Brain tumor segmentation using deep convolutional neural network. Proceedings of BRATS-MICCAI, 2016.
Search in Google Scholar Back to article
[106] MICCAI. about MICCAI. 2016 [cited 2016; Available from: http://www.miccai.org/organization].
Search in Google Scholar Back to article
[107] Veta M, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS one. 2013;8(7): e70221.10.1371/journal.pone.0070221372642123922958
Search in Google Scholar Back to article
[108] Kang J. et al. Neuron sparseness versus connection sparseness in deep neural network for large vocabulary speech recognition. in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2015. IEEE.
Search in Google Scholar Back to article
[109] Kleć M, Koržinek D. Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition. Computer Science. 2015;16(2): 133-144.10.7494/csci.2015.16.2.133
Search in Google Scholar Back to article
[110] Oyallon E, Mallat S, Sifre L. Generic deep networks with wavelet scattering. arXiv preprint arXiv:1312.5940, 2013.
Search in Google Scholar Back to article
[111] Hassairi S, Ejbali R, Zaied M. Supervised image classification using Deep Convolutional Wavelets Network. in Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on. 2015. IEEE.10.1109/ICTAI.2015.49
Search in Google Scholar Back to article

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Abstract

Paradigm

My account