Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Review Article

Survey on the Techniques for Classification and Identification of Brain Tumour Types from MRI Images Using Deep Learning Algorithms

Author(s): Gayathri Devi K. and Kishore Balasubramanian*

Volume 16, Issue 9, 2023

Published on: 31 July, 2023

Article ID: e010623217564 Pages: 16

DOI: 10.2174/2666255816666230601150351

Price: $65

conference banner
Abstract

A tumour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and characteristics and have different treatments. Detection of a tumour in the earlier stages makes the treatment easier. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumours. This paper provides a systematic literature survey of techniques for brain tumour segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques. This survey covers publicly available datasets, enhancement techniques, segmentation, feature extraction, and the classification of three different types of brain tumours that include gliomas, meningioma, and pituitary and deep learning algorithms implemented for brain tumour analysis. Finally, this survey provides all the important literature on the detection of brain tumours with their developments.

Graphical Abstract

[1]
V. Rao, M S Sarabi, and a Jaiswal, "Brain tumor segmentation with deep learning", In: MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). Munich: Springer International Publishing, 2015, pp. 56-59.
[2]
A. Işın, C. Direkoğlu, and M. Şah, "Review of MRI-based brain tumor image segmentation using deep learning methods", Procedia Comput. Sci., vol. 102, pp. 317-324, 2016.
[http://dx.doi.org/10.1016/j.procs.2016.09.407]
[3]
P. Moeskops, "Deep learning for multi-task medical image segmentation in multiple modalities", In: MICCAI, Springer International Publishing: Cham, 2016, pp. 478-486.
[http://dx.doi.org/10.1007/978-3-319-46723-8_55]
[4]
K. Kamnitsas, "DeepMedic for Brain Tumor Segmentation", In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries., Springer International Publishing: Cham, 2016, pp. 138-149.
[http://dx.doi.org/10.1007/978-3-319-55524-9_14]
[5]
X. Hao, Y. Wu, G. Song, Z. Li, Y. Fan, and Y. Zhang, "Brain tumor segmentation using a fully convolutional neural network with conditional random fields", In: International workshop on brain lesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, 2016, pp. 75-87.
[6]
S. Hussain, S.M. Anwar, and M. Majid, "Brain tumor segmentation using cascaded deep convolutional neural network", Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2017, pp. 1998-2001, 2017.
[http://dx.doi.org/10.1109/EMBC.2017.8037243] [PMID: 29060287]
[7]
I. Ramírez, A. Martín, and E. Schiavi, "Optimization of a variational model using deep learning: An application to brain tumor segmentation,"", 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, pp. 631-634, 2018.
[http://dx.doi.org/10.1109/ISBI.2018.8363654]
[8]
R. Gruetzemacher, A. Gupta, and D. Paradice, "3D deep learning for detecting pulmonary nodules in CT scans", J. Am. Med. Inform. Assoc., vol. 25, no. 10, pp. 1301-1310, 2018.
[http://dx.doi.org/10.1093/jamia/ocy098] [PMID: 30137371]
[9]
T.C. Mok, and A. Chung,
“Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks” In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science, vol 11383. Springer, Cham. [http://dx.doi.org/10.1007/978-3-030-11723-8_7]
[10]
Y. Zhuge, A.V. Krauze, H. Ning, J.Y. Cheng, B.C. Arora, K. Camphausen, and R.W. Miller, "Brain tumor segmentation using holistically nested neural networks in MRI images", Med. Phys., vol. 44, no. 10, pp. 5234-5243, 2017.
[http://dx.doi.org/10.1002/mp.12481] [PMID: 28736864]
[11]
G. Latif, G. Ben Brahim, D.N.F.A. Iskandar, A. Bashar, and J. Alghazo, "Glioma tumors’ classification using deep-neural-network-based features with SVM classifier", Diagnostics, vol. 12, no. 4, p. 1018, 2022.
[http://dx.doi.org/10.3390/diagnostics12041018] [PMID: 35454066]
[12]
M.J. Sheller, G.A. Reina, B. Edwards, and J. Martin, "Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation", In: International MICCAI Brainlesion Workshop Springer, 2018, pp. 92-104.
[13]
H. Byale, G.M. Lingaraju, and S. Sivasubramanian, "Automatic segmentation and classification of brain tumors using machine learning techniques", Int. J. Appl. Eng. Res., vol. 13, no. 14, pp. 11686-11692, 2018.
[14]
H.C. Shin, N.A. Tenenholtz, J.K. Rogers, C.G. Schwarz, M.L. Senjem, and J.L. Gunter, "Medical image synthesis for data augmentation and anonymization using generative adversarial networks", In: Int. Workshop Simul. Synth. Med. imaging. Springer, 2018, pp. 1-11.
[http://dx.doi.org/10.1007/978-3-030-00536-8_1]
[15]
A. Jesson, and T. Arbel, Brain tumor segmentation using a 3D FCN with multi-scale loss. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries., Springer International Publishing, 2018, pp. 392-402.
[http://dx.doi.org/10.1007/978-3-319-75238-9_34]
[16]
S. Kumar, A. Negi, J.N. Singh, and H. Verma, "A Deep Learning for Brain Tumor MRI Images Semantic Segmentation Using FCN", 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 1-4, 2018.
[http://dx.doi.org/10.1109/CCAA.2018.8777675]
[17]
G. Wang, W. Li, M.A. Zuluaga, R. Pratt, P.A. Patel, M. Aertsen, T. Doel, A.L. David, J. Deprest, S. Ourselin, and T. Vercauteren, "Interactive medical image segmentation using deep learning with image-specific fine-tuning", IEEE Trans. Med. Imaging, vol. 37, no. 7, pp. 1562-1573, 2018.
[http://dx.doi.org/10.1109/TMI.2018.2791721] [PMID: 29969407]
[18]
X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, "A deep learning model integrating FCNNs and CRFs for brain tumor segmentation", Med. Image Anal., vol. 43, pp. 98-111, 2018.
[http://dx.doi.org/10.1016/j.media.2017.10.002] [PMID: 29040911]
[19]
Y. Hu, and Y. Xia, "3D deep neural network-based brain tumor segmentation using multimodality magnetic resonance sequences", In: Third International Workshop. Springer International Publishing: Quebec City, QC, Canada, 2017, pp. 423-434.
[20]
V. Kiruthika Lakshmi, C.A. Feroz, and J. Asha Jenia Merlin, "Automated Detection and Segmentation of Brain Tumor Using Genetic Algorithm", In: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018, pp. 583-589.
[http://dx.doi.org/10.1109/ICSSIT.2018.8748487]
[21]
G. Madhupriya, N.M. Guru, S. Praveen, and B. Nivetha, "Brain Tumor Segmentation with Deep Learning Technique", In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 758-763.
[http://dx.doi.org/10.1109/ICOEI.2019.8862575]
[22]
R. Thillaikkarasi, and S. Saravanan, "An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM", J. Med. Syst., vol. 43, no. 4, p. 84, 2019.
[http://dx.doi.org/10.1007/s10916-019-1223-7] [PMID: 30810822]
[23]
T. Yang, J. Song, and L. Li, "A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI", Biocybern. Biomed. Eng., vol. 39, no. 3, pp. 613-623, 2019.
[http://dx.doi.org/10.1016/j.bbe.2019.06.003]
[24]
S. Alqazzaz, X. Sun, X. Yang, and L. Nokes, "Automated brain tumor segmentation on multi-modal MR image using SegNet", Computat. Visual Media, vol. 5, no. 2, pp. 209-219, 2019.
[http://dx.doi.org/10.1007/s41095-019-0139-y]
[25]
G. Wang, M.A. Zuluaga, W. Li, R. Pratt, P.A. Patel, M. Aertsen, T. Doel, A.L. David, J. Deprest, S. Ourselin, and T. Vercauteren, "DeepIGeoS: A deep interactive geodesic framework for medical image segmentation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 7, pp. 1559-1572, 2019.
[http://dx.doi.org/10.1109/TPAMI.2018.2840695] [PMID: 29993532]
[26]
Y. Zhou, "Holistic brain tumor screening and classification based on DenseNet and recurrent neural network", In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries., Springer International Publishing, 2019.
[http://dx.doi.org/10.1007/978-3-030-11723-8_21]
[27]
K. Pathak, M. Pavthawala, N. Patel, D. Malek, V. Shah, and B. Vaidya, "Classification of Brain Tumor Using Convolutional Neural Network", 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 128-132, 2019.
[http://dx.doi.org/10.1109/ICECA.2019.8821931]
[28]
A. Veeramuthu, S. Meenakshi, and K. Ashok Kumar, "A neural network based deep learning approach for efficient segmentation of brain tumor medical image data", J. Intell. Fuzzy Syst., vol. 36, no. 5, pp. 4227-4234, 2019.
[http://dx.doi.org/10.3233/JIFS-169980]
[29]
Y. Xu, A. Hosny, R. Zeleznik, C. Parmar, T. Coroller, I. Franco, R.H. Mak, and H.J.W.L. Aerts, "Deep learning predicts lung cancer treatment response from serial medical imaging", Clin. Cancer Res., vol. 25, no. 11, pp. 3266-3275, 2019.
[http://dx.doi.org/10.1158/1078-0432.CCR-18-2495] [PMID: 31010833]
[30]
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li, "Deep learning-based image segmentation on multimodal medical imaging", IEEE Trans. Radiat. Plasma Med. Sci., vol. 3, no. 2, pp. 162-169, 2019.
[http://dx.doi.org/10.1109/TRPMS.2018.2890359] [PMID: 34722958]
[31]
M.I. Razzak, M. Imran, and G. Xu, "Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks", IEEE J. Biomed. Health Inform., vol. 23, no. 5, pp. 1911-1919, 2019.
[http://dx.doi.org/10.1109/JBHI.2018.2874033] [PMID: 30295634]
[32]
U. Baid, S. Talbar, S. Rane, S. Gupta, M.H. Thakur, A. Moiyadi, N. Sable, M. Akolkar, and A. Mahajan, "A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas", Front. Comput. Neurosci., vol. 14, p. 10, 2020.
[http://dx.doi.org/10.3389/fncom.2020.00010] [PMID: 32132913]
[33]
M. Ali, S.O. Gilani, A. Waris, K. Zafar, and M. Jamil, "Brain tumour image segmentation using deep networks", IEEE Access, vol. 8, pp. 153589-153598, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3018160]
[34]
N. Ghassemi, A. Shoeibi, and M. Rouhani, "Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images", Biomed. Signal Process. Control, vol. 57, no. 101678, p. 101678, 2020.
[http://dx.doi.org/10.1016/j.bspc.2019.101678]
[35]
S. Maharjan, A. Alsadoon, P.W.C. Prasad, T. Al-Dalain, and O.H. Alsadoon, "A novel enhanced softmax loss function for brain tumour detection using deep learning", J. Neurosci. Methods, vol. 330, no. 108520, p. 108520, 2020.
[http://dx.doi.org/10.1016/j.jneumeth.2019.108520] [PMID: 31734325]
[36]
Z. Jia, and D. Chen,
"Brain Tumor Identification and Classification of MRI images using deep learning techniques," in IEEE Access, [http://dx.doi.org/10.1109/ACCESS.2020.3016319]
[37]
M.M. Badža, and M.Č. Barjaktarović, "Classification of brain tumors from MRI images using a convolutional neural network", Appl. Sci., vol. 10, no. 6, p. 1999, 2020.
[http://dx.doi.org/10.3390/app10061999]
[38]
F. Renard, S. Guedria, N.D. Palma, and N. Vuillerme, "Variability and reproducibility in deep learning for medical image segmentation", Sci. Rep., vol. 10, no. 1, p. 13724, 2020.
[http://dx.doi.org/10.1038/s41598-020-69920-0] [PMID: 32792540]
[39]
S. Vijh, S. Sharma, and P. Gaurav, "Brain tumor segmentation using OTSU embedded adaptive particle swarm optimization method and convolutional neural network", In: Data Visualization and Knowledge Engineering, Springer International Publishing: Cham , 2020, pp. 171-194.
[http://dx.doi.org/10.1007/978-3-030-25797-2_8]
[40]
A. Mashiat, R.R. Akhlaque, F.H. Fariha, T. Reza, M.A. Rahman, and M.Z. Parvez, "Detection of Brain Tumor and Identification of Tumor Region Using Deep Neural Network On FMRI Images", 2020 International Conference on Machine Learning and Cybernetics (ICMLC), Adelaide, Australia, pp. 124-130, 2020.
[http://dx.doi.org/10.1109/ICMLC51923.2020.9469565]
[41]
M.T. Bennai, Z. Guessoum, S. Mazouzi, S. Cormier, and M. Mezghiche, "A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images", Artif. Intell. Med., vol. 110, no. 101980, p. 101980, 2020.
[http://dx.doi.org/10.1016/j.artmed.2020.101980] [PMID: 33250150]
[42]
W. Deng, Q. Shi, M. Wang, B. Zheng, and N. Ning, "Deep learning-based HCNN and CRF-RRNN model for brain tumor segmentation", IEEE Access, vol. 8, pp. 26665-26675, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2966879]
[43]
H. Jiang, Z. Diao, and Y.D. Yao, "Deep learning techniques for tumor segmentation: A review", J. Supercomput., vol. 78, no. 2, pp. 1807-1851, 2022.
[http://dx.doi.org/10.1007/s11227-021-03901-6]
[44]
T. Zhou, S. Canu, P. Vera, and S. Ruan, "Latent correlation representation learning for brain tumor segmentation with missing MRI modalities", IEEE Trans. Image Process., vol. 30, pp. 4263-4274, 2021.
[http://dx.doi.org/10.1109/TIP.2021.3070752] [PMID: 33830924]
[45]
R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi, S. Anari, M. Naseri, and M. Bendechache, "Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images", Sci. Rep., vol. 11, no. 1, p. 10930, 2021.
[http://dx.doi.org/10.1038/s41598-021-90428-8] [PMID: 34035406]
[46]
M. Rizwan, A. Shabbir, A.R. Javed, M. Shabbir, T. Baker, and D. Al-Jumeily Obe, "Brain tumor and glioma grade classification using Gaussian convolutional neural network", IEEE Access, vol. 10, pp. 29731-29740, 2022.
[http://dx.doi.org/10.1109/ACCESS.2022.3153108]
[47]
J. Kang, Z. Ullah, and J. Gwak, "MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers", Sensors, vol. 21, no. 6, p. 2222, 2021.
[http://dx.doi.org/10.3390/s21062222] [PMID: 33810176]
[48]
M.I. Sharif, M.A. Khan, M. Alhussein, K. Aurangzeb, and M. Raza, "A decision support system for multimodal brain tumor classification using deep learning", Complex & Intelligent Systems, vol. 8, no. 4, pp. 3007-3020, 2022.
[http://dx.doi.org/10.1007/s40747-021-00321-0]
[49]
Y. Li, Z. Wang, L. Yin, Z. Zhu, G. Qi, and Y. Liu, "X-Net: A dual encoding–decoding method in medical image segmentation", Vis. Comput., vol. 39, pp. 2223-2233, 2023.
[http://dx.doi.org/10.1007/s00371-021-02328-7]
[50]
A.H. Khan, S. Abbas, M.A. Khan, U. Farooq, W.A. Khan, S.Y. Siddiqui, and A. Ahmad, "Intelligent model for brain tumor identification using deep learning", Appl. Comput. Intell. Soft Comput., vol. 2022, pp. 1-10, 2022.
[http://dx.doi.org/10.1155/2022/8104054]
[51]
F.J. Díaz-Pernas, M. Martínez-Zarzuela, M. Antón-Rodríguez, and D. González-Ortega, "A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network", Health care, vol. 9, no. 2, p. 153, 2021.
[http://dx.doi.org/10.3390/healthcare9020153] [PMID: 33540873]
[52]
D.S. Wankhede, and R. Selvarani, "Dynamic architecture based deep learning approach for glioblastoma brain tumor survival prediction", Neurosci. Informat., vol. 2, no. 4, p. 100062, 2022.
[http://dx.doi.org/10.1016/j.neuri.2022.100062]
[53]
Y. Xu, X. He, G. Xu, G. Qi, K. Yu, L. Yin, P. Yang, Y. Yin, and H. Chen, "A medical image segmentation method based on multi-dimensional statistical features", Front. Neurosci., vol. 16, p. 1009581, 2022.
[http://dx.doi.org/10.3389/fnins.2022.1009581] [PMID: 36188458]
[54]
M. Faysal Ahamed, M. Islam, T. Hossain, K. Syfullah, and O. Sarkar,
"Classification and Segmentation on Multi-regional Brain Tumors Using Volumetric Images of MRI with Customized 3D UNet Framework", Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data- driven and Industrial Computing. Springer, Singapore. [http://dx.doi.org/10.1007/978-981-19-7528-8_18]
[55]
Z. Zhu, X. He, G. Qi, Y. Li, B. Cong, and Y. Liu, "Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI", Inf. Fusion, vol. 91, pp. 376-387, 2023.
[http://dx.doi.org/10.1016/j.inffus.2022.10.022]
[56]
G. Latif, G. Ben Brahim, D.N.F.A. Iskandar, A. Bashar, and J. Alghazo, "Glioma Tumors’ Classification Using Deep-Neural-Network- Based Features with SVM Classifier", Diagnostics, vol. 12, p. 1018, 2022.
[http://dx.doi.org/10.3390/diagnostics12041018]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy