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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Brain Tumor Detection Using Machine Learning and Deep Learning: A Review

Author(s): Venkatesh S. Lotlikar*, Nitin Satpute and Aditya Gupta

Volume 18, Issue 6, 2022

Published on: 20 January, 2022

Article ID: e230921196757 Pages: 19

DOI: 10.2174/1573405617666210923144739

Price: $65

Abstract

According to the International Agency for Research on Cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as Magnetic Resonance Iimaging (MRI) and computed tomography scans using a computer-aided design. Machine learning and deep learning techniques have gained significance among researchers in medical fields, especially Convolutional Neural Networks (CNN), due to their ability to analyze large amounts of complex image data and perform classification. The objective of this review article is to present an exhaustive study of techniques such as preprocessing, machine learning, and deep learning that have been adopted in the last 15 years and based on it to present a detailed comparative analysis. The challenges encountered by researchers in the past for tumor detection have been discussed along with the future scopes that can be taken by the researchers as the future work. Clinical challenges that are encountered have also been discussed, which are missing in existing review articles.

Keywords: Brain tumor, magnetic resonance imaging, preprocessing, machine learning, deep learning, convolutional neural networks.

Graphical Abstract

[1]
Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer diagnosis using deep learning: A bibliographic review. Cancers (Basel) 2019; 11(9): 1235.
[http://dx.doi.org/10.3390/cancers11091235] [PMID: 31450799]
[2]
Khan MA, Lali IU, Rehman A, et al. Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 2019; 82(6): 909-22.
[http://dx.doi.org/10.1002/jemt.23238] [PMID: 30801840]
[3]
Manjusha S, Neelima P, Ananya B, Bhavitha KVNSD, Narayana VL. Brain tumor detection using convolutional neural networks and deep learning concepts. J Eng Sci 2018; 0377-9254.
[4]
Mortazavi SMJ, Mortazavi SAR, Paknahad M. Cancers of the brain and CNS: Global patterns and trends in incidence. J Biomed Phys Eng 2018; 8(1): 151-2.
[PMID: 29732351]
[5]
Lin T, Zhao P, Jiang Y, et al. Blood–brain-barrier-penetrating albumin nanoparticles for biomimetic drug delivery via albumin-binding protein pathways for anti-glioma therapy. ACS Nano 2016; 10(11): 9999-10012.
[http://dx.doi.org/10.1021/acsnano.6b04268] [PMID: 27934069]
[6]
Parodi A, Rudzińska M, Deviatkin AA, Soond SM, Baldin AV, Zamyatnin AA Jr. Established and emerging strategies for drug delivery across the blood-brain barrier in brain cancer. Pharmaceutics 2019; 11(5): 245.
[http://dx.doi.org/10.3390/pharmaceutics11050245] [PMID: 31137689]
[7]
Chandra SK, Bajpai MK. Effective algorithm for benign brain tumor detection using fractional calculus. TENCON 2018-2018 IEEE Region 10 Conference. In: IEEE; 2018; pp. 2408-13.
[http://dx.doi.org/10.1109/TENCON.2018.8650163]
[8]
Rao BD, Goswami MM. A comprehensive study of features used for brain tumor detection and segmentation from Mr images. 2017 Innovations in Power and Advanced Computing Technologies (i-PACT). In: IEEE; 2017; pp. 1-6.
[9]
Farmanfarma KK, Mohammadian M, Shahabinia Z, Hassanipour S, Salehiniya H. Brain cancer in the world: An epidemiological review. World Can Res J 2019; 6: 5.
[10]
Lee B, Kang U, Chang H, Cho KH. The hidden control architecture of complex brain networks. iScience 2019; 13: 154-62.
[http://dx.doi.org/10.1016/j.isci.2019.02.017] [PMID: 30844695]
[11]
Hosseinzadeh M, Salmani S, Majles Ara MH, Mohajer S. The simple optical methods for early diagnosis of selected benign and malignant brain tumors of humans. J Nonlinear Opt Phys Mater 2018; 27(03): 1850033.
[http://dx.doi.org/10.1142/S0218863518500339]
[12]
Ahn J, Park MY, Kang MY, Shin IS, An S, Kim HR. Occupational lead exposure and brain tumors: Systematic review and meta-analysis. Int J Environ Res Public Health 2020; 17(11): 3975.
[http://dx.doi.org/10.3390/ijerph17113975] [PMID: 32503353]
[13]
S Tandel G, Biswas M, G Kakde O, et al. A review on a deep learning perspective in brain cancer classification. Cancers (Basel) 2019; 11(1): 111.
[http://dx.doi.org/10.3390/cancers11010111] [PMID: 30669406]
[14]
Tang W, Fan W, Lau J, Deng L, Shen Z, Chen X. Emerging blood-brain-barrier-crossing nanotechnology for brain cancer theranostics. Chem Soc Rev 2019; 48(11): 2967-3014.
[http://dx.doi.org/10.1039/C8CS00805A] [PMID: 31089607]
[15]
Villa C, Miquel C, Mosses D, Bernier M, Di Stefano AL. The 2016 World Health Organization classification of tumours of the central nervous system. Presse Med 2018; 47(11-12 Pt 2): e187-200.
[http://dx.doi.org/10.1016/j.lpm.2018.04.015] [PMID: 30449638]
[16]
Mendes M, Sousa JJ, Pais A, Vitorino C. Targeted theranostic nanoparticles for brain tumor treatment. Pharmaceutics 2018; 10(4): 181.
[http://dx.doi.org/10.3390/pharmaceutics10040181] [PMID: 30304861]
[17]
Dandıl E, Çakıroğlu M, Ekşi Z. Computer-aided diagnosis of malign and benign brain tumors on MR images. International Conference on ICT Innovations. 157-66.
[18]
Kutlu H, Avcı E. A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors (Basel) 2019; 19(9): 1992.
[http://dx.doi.org/10.3390/s19091992] [PMID: 31035406]
[19]
Kumar S, Mankame DP. Optimization driven deep convolution neural network for brain tumor classification. Biocybern Biomed Eng 2020; 40(3): 1190-204.
[http://dx.doi.org/10.1016/j.bbe.2020.05.009]
[20]
Afshar P, Mohammadi A, Plataniotis KN. Brain tumor type classification via capsule networks. 25th IEEE International Conference on Image Processing (ICIP). 3129-3.
[http://dx.doi.org/10.1109/ICIP.2018.8451379]
[21]
Ghaffari M, Sowmya A, Oliver R. Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the brats 2012-2018 challenges. IEEE Rev Biomed Eng 2020; 13: 156-68.
[http://dx.doi.org/10.1109/RBME.2019.2946868] [PMID: 31613783]
[22]
Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci 2016; 102: 317-24.
[http://dx.doi.org/10.1016/j.procs.2016.09.407]
[23]
Zhou M, Scott J, Chaudhury B, et al. Radiomics in brain tumor: Image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 2018; 39(2): 208-16.
[http://dx.doi.org/10.3174/ajnr.A5391] [PMID: 28982791]
[24]
Alluri HV, Narayana TV, Ramya BN, Rajesh B. Detection and diagnosis of brain tumor using segmentation and classification methods: A review. Int J Technol Res Eng 2013; 2347-4718.
[25]
Kapoor L, Thakur S. A survey on brain tumor detection using image processing techniques. 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence. 582-5.
[http://dx.doi.org/10.1109/CONFLUENCE.2017.7943218]
[26]
Wadhwa A, Bhardwaj A, Singh Verma V. A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019; 61: 247-59.
[http://dx.doi.org/10.1016/j.mri.2019.05.043] [PMID: 31200024]
[27]
Kurup RV, Sowmya V, Soman KP. Effect of data pre-processing on brain tumor classification using capsulenet. ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management 2019; 110-9.
[28]
Jeong HJ, Park KS, Ha YG. Image preprocessing for efficient training of yolo deep learning networks. 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). 635-7.
[http://dx.doi.org/10.1109/BigComp.2018.00113]
[29]
Pitaloka DA, Wulandari A, Basaruddin T, Liliana DY. Enhancing CNN with preprocessing stage in automatic emotion recognition. Procedia Comput Sci 2017; 116: 523-9.
[http://dx.doi.org/10.1016/j.procs.2017.10.038]
[30]
Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci 2017; 120: 580-7.
[http://dx.doi.org/10.1016/j.procs.2017.11.282]
[31]
Devkota B, Alsadoon A, Prasad PWC, Singh AK, Elchouemi A. Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Comput Sci 2018; 125: 115-23.
[http://dx.doi.org/10.1016/j.procs.2017.12.017]
[32]
Somasundaram K, Mercina JH, Magesh Kalaiselvi ST. Brain portion extraction scheme using region growing and morphological operation from MRI of human head scans. IJCSE 2018; 6(4): 298-302.
[33]
Gupta N, Bhatele P, Khanna P. Glioma detection on brain MRIs using texture and morphological features with ensemble learning. Biomed Signal Process Control 2019; 47: 115-25.
[http://dx.doi.org/10.1016/j.bspc.2018.06.003]
[34]
Ripollés P, Marco-Pallarés J, de Diego-Balaguer R, et al. Analysis of automated methods for spatial normalization of lesioned brains. Neuroimage 2012; 60(2): 1296-306.
[http://dx.doi.org/10.1016/j.neuroimage.2012.01.094] [PMID: 22305954]
[35]
Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G. A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 2019; 7: 36266-73.
[http://dx.doi.org/10.1109/ACCESS.2019.2904145]
[36]
Kociolek M, Strzelecki M, Szymajda S. On the influence of the image normalization scheme on texture classification accuracy. Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) 2018; 152-7.
[http://dx.doi.org/10.23919/SPA.2018.8563397]
[37]
Razzak MI, Imran M, Xu G. Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inform 2019; 23(5): 1911-9.
[http://dx.doi.org/10.1109/JBHI.2018.2874033] [PMID: 30295634]
[38]
Febrianto DC, Soesanti I, Nugroho HA. Convolutional neural network for brain tumor detection. IOP Conf Ser Mater Sci Eng. 771(1): 012031.
[http://dx.doi.org/10.1088/1757-899X/771/1/012031]
[39]
Goyal B, Agrawal S, Sohi BS. Noise issues prevailing in various types of medical images. Biomed Pharmacol J 2018; 11(3): 1227-37.
[http://dx.doi.org/10.13005/bpj/1484]
[40]
Faisal A, Parveen S, Badsha S, Sarwar H. An improved image denoising and segmentation approach for detecting tumor from 2-d MRI brain images. 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). 452-7.
[http://dx.doi.org/10.1109/ACSAT.2012.35]
[41]
Vaishali S, Rao KK, Rao GS. A review on noise reduction methods for brain MRI images. 2015 International Conference on Signal Processing and Communication Engineering Systems. 363-5.
[http://dx.doi.org/10.1109/SPACES.2015.7058284]
[42]
Lahmiri S, Boukadoum M. Hybrid Wiener and partial differential equations filter for biomedical image denoising. 2016 14th IEEE International New Circuits and Systems Conference (NEWCAS). 1-4.
[http://dx.doi.org/10.1109/NEWCAS.2016.7604754]
[43]
Kollem S, Reddy KR, Rao DS. Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical image denoising. Multimedia Tools Appl 2020; 80(2): 2663-89.
[http://dx.doi.org/10.1007/s11042-020-09745-1]
[44]
Zeng Y, Zhang B, Zhao W, et al. Magnetic resonance image denoising algorithm based on cartoon, texture, and residual parts. Comput Math Methods Med 2020; 2020: 1405647.
[http://dx.doi.org/10.1155/2020/1405647] [PMID: 32411276]
[45]
Phophalia A, Rajwade A, Mitra SK. Rough set based image denoising for brain MR images. Signal Processing 2014; 103: 24-35.
[http://dx.doi.org/10.1016/j.sigpro.2014.01.029]
[46]
Kalavathi P, Prasath VB. Methods on skull stripping of MRI head scan images-a review. J Digit Imaging 2016; 29(3): 365-79.
[http://dx.doi.org/10.1007/s10278-015-9847-8] [PMID: 26628083]
[47]
Roy S, Maji P. A simple skull stripping algorithm for brain MRI. 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR). 1-6.
[http://dx.doi.org/10.1109/ICAPR.2015.7050671]
[48]
Yilmaz B, Durdu A, Emlik GD. A new method for skull stripping in brain MRI using multistable cellular neural networks. Neural Comput Appl 2018; 29(8): 79-95.
[http://dx.doi.org/10.1007/s00521-016-2834-2]
[49]
Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 2014; 42(1): 21-57.
[http://dx.doi.org/10.1007/s10462-012-9328-0]
[50]
Chen K, Shen J, Scalzo F. Skull Stripping Using Confidence Segmentation Convolution Neural Network: 13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19 – 21, 2018, Proceedings.Advances in Visual Computing ISVC. Cham: Springer 2018.
[http://dx.doi.org/10.1007/978-3-030-03801-4_2]
[51]
Leal N, Varela EZ. A New approach on skull stripping of brain MRI based on saliency detection using dictionary learning and sparse coding. Prospectiva 2019; 17(2): 4.
[52]
Fatima A, Shahid AR, Raza B, Madni TM, Janjua UI. State-of-the-art traditional to the machine-and deep-learning-based skull stripping techniques, models, and algorithms. J Digit Imaging 2020; 33(6): 1443-64.
[http://dx.doi.org/10.1007/s10278-020-00367-5] [PMID: 32666364]
[53]
Won CS. Constrained optimization for image reshaping with soft conditions. IEEE Access 2018; 6: 54823-33.
[http://dx.doi.org/10.1109/ACCESS.2018.2872497]
[54]
Ghosh S, Das N, Nasipuri M. Reshaping inputs for convolutional neural network: Some common and uncommon methods. Pattern Recognit 2019; 93: 79-94.
[http://dx.doi.org/10.1016/j.patcog.2019.04.009]
[55]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint 2014; 14091556 .
[56]
Lotan E, Jain R, Razavian N, Fatterpekar GM, Lui YW. State of the art: Machine learning applications in glioma imaging. AJR Am J Roentgenol 2019; 212(1): 26-37.
[http://dx.doi.org/10.2214/AJR.18.20218] [PMID: 30332296]
[57]
Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics 2017; 37(2): 505-15.
[http://dx.doi.org/10.1148/rg.2017160130] [PMID: 28212054]
[58]
Fu GS, Levin-Schwartz Y, Lin QH, Zhang D. Machine learning for medical imaging. J Healthc Eng 2019; 2019: 9874591.
[http://dx.doi.org/10.1155/2019/9874591] [PMID: 31183031]
[59]
K KK, T MD, S M. An efficient method for brain tumor detection using texture features and SVM classifier in MR images. Asian Pac J Cancer Prev 2018; 19(10): 2789-94.
[PMID: 30360607]
[60]
Nagalkar VJ, Sarate GG. Brain tumor detection and identification using support vector machine. Brain 2019; 6(12): 2020-3.
[61]
Nandpuru HB, Salankar SS, Bora VR. MRI brain cancer classification using support vector machine. 2014 IEEE Students Conference on Electrical, Electronics and Computer Science. 1-6.
[http://dx.doi.org/10.1109/SCEECS.2014.6804439]
[62]
Srinivas B, Rao GS. Performance evaluation of fuzzy C means segmentation and support vector machine classification for MRI brain tumor.Soft computing for problem solving. Singapore: Springer 2019; pp. 355-67.
[http://dx.doi.org/10.1007/978-981-13-1595-4_29]
[63]
Kharrat A, Halima MB, Ayed MB. MRI brain tumor classification using support vector machines and meta-heuristic method. 15th International Conference on Intelligent Systems Design and Applications (ISDA). 446-51.
[http://dx.doi.org/10.1109/ISDA.2015.7489271]
[64]
Kharrat A, Benamrane N, Messaoud MB, Abid M. Evolutionary support vector machine for parameters optimization applied to medical diagnostic. VISAPP 2011; 201-4.
[65]
Kharrat A, Gasmi K, Messaoud MB, Benamrane N, Abid M. Medical image classification using an optimal feature extraction algorithm and a supervised classifier technique. Int J Softw Sci Comput Intell 2011; 3(2): 19-33.
[http://dx.doi.org/10.4018/jssci.2011040102]
[66]
Wasule V, Sonar P. Classification of brain MRI using SVM and KNN classifier. 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). 218-23.
[http://dx.doi.org/10.1109/SSPS.2017.8071594]
[67]
Panda B, Panda CS. A review on brain tumor classification methodologies. Int J Sci Res Sci Technol 2019; 6(6): 346-59.
[http://dx.doi.org/10.32628/IJSRST20717]
[68]
Pugalenthi R, Rajakumar MP, Ramya J, Rajinikanth V. Evaluation and classification of the brain tumor MRI using machine learning technique. J Control Eng Appl Inform 2019; 21(4): 12-21.
[69]
Sharma K, Kaur A, Gujral S. A review on various brain tumor detection techniques in brain MRI images. IOSR J Eng 2014; 4(05): 6-12.
[http://dx.doi.org/10.9790/3021-04530612]
[70]
Comelli A, Stefano A, Russo G, et al. K-nearest neighbor driving active contours to delineate biological tumor volumes. Eng Appl Artif Intell 2019; 81: 133-44.
[http://dx.doi.org/10.1016/j.engappai.2019.02.005]
[71]
Comelli A, Stefano A, Benfante V, Russo G. Normal and abnormal tissue classification in positron emission tomography oncological studies. Pattern Recognit Image Anal 2018; 28(1): 106-13.
[http://dx.doi.org/10.1134/S1054661818010054]
[72]
Armand S, Watelain E, Roux E, Mercier M, Lepoutre FX. Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture 2007; 25(3): 475-84.
[http://dx.doi.org/10.1016/j.gaitpost.2006.05.014] [PMID: 16837198]
[73]
Okfalisa I, Gazalba M, Reza NGI. Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). 294-8.
[http://dx.doi.org/10.1109/ICITISEE.2017.8285514]
[74]
Parvin H, Alizadeh H, Minaei-Bidgoli B. MKNN: Modified k-nearest neighbor. Proceedings of the World Congress on Engineering and Computer Science. October 22 - 24, 2008; San Francisco, USA. 2008.
[75]
Kaur T, Saini BS, Gupta S. An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm. Multimedia Tools Appl 2019; 78(15): 21853-90.
[http://dx.doi.org/10.1007/s11042-019-7498-3]
[76]
Vardasca R, Vaz L, Mendes J. Classification and decision making of medical infrared thermal images.Classification in BioApps. Cham: Springer 2018; pp. 79-104.
[http://dx.doi.org/10.1007/978-3-319-65981-7_4]
[77]
Amrane M, Oukid S, Gagaoua I, Ensar İ. Breast cancer classification using machine learning.Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT). 1-4.
[http://dx.doi.org/10.1109/EBBT.2018.8391453]
[78]
Zaw HT, Maneerat N, Win KY. Brain tumor detection based on Naïve Bayes Classification. 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST). 1-4.
[http://dx.doi.org/10.1109/ICEAST.2019.8802562]
[79]
Raju AR, Pabboju S, Rao RR. Brain image classification using dual-tree M-band wavelet transform and naïve bayes classifier.Intelligent Computing in Engineering. Singapore: Springer 2020; pp. 635-42.
[http://dx.doi.org/10.1007/978-981-15-2780-7_69]
[80]
Sonawane JM, Gaikwad SD, Prakash G. Microarray data classification using dual tree m-band wavelet features. Int J Adv Signal Image Sci 2017; 3(1): 19-24.
[http://dx.doi.org/10.29284/IJASIS.3.1.2017.19-24]
[81]
Singh A, Lakshmiganthan R. Impact of different data types on classifier performance of random forest, naive bayes, and k-nearest neighbors algorithms. Int J Adv Comput Sci Appl 2017; 8(12): 12-22.
[http://dx.doi.org/10.14569/IJACSA.2017.081201]
[82]
Chen J, Li K, Tang Z, et al. A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans Parallel Distrib Syst 2016; 28(4): 919-33.
[http://dx.doi.org/10.1109/TPDS.2016.2603511]
[83]
Anitha R, Siva Sundhara Raja D. Development of computer‐aided approach for brain tumor detection using random forest classifier. Int J Imaging Syst Technol 2018; 28(1): 48-53.
[http://dx.doi.org/10.1002/ima.22255]
[84]
Soltaninejad M, Zhang L, Lambrou T, Yang G, Allinson N, Ye X. MRI brain tumor segmentation using random forests and fully convolutional networks arXiv preprint 2019; arXiv:190906337.
[85]
Hatami T, Hamghalam M, Reyhani-Galangashi O, Mirzakuchaki S. A machine learning approach to brain tumors segmentation using adaptive random forest algorithm. 5th Conference on Knowledge Based Engineering and Innovation (KBEI).
[http://dx.doi.org/10.1109/KBEI.2019.8735072]
[86]
Lefkovits L, Lefkovits S, Szilágyi L. Brain Tumor Segmentation with Optimized Random Forest. In: Crimi A, Menze B, Maier O, Reyes M, Winzeck S, Handels H, Eds. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer 2016.
[http://dx.doi.org/10.1007/978-3-319-55524-9_9]
[87]
El-Melegy MT, El-Magd KMA, Ali SA, Hussain KF, Mahdy YB. Ensemble of multiple classifiers for automatic multimodal brain tumor segmentation. International Conference on Innovative Trends in Computer Engineering (ITCE). 58-63.
[http://dx.doi.org/10.1109/ITCE.2019.8646431]
[88]
Kim D. brain tumor detection: 2 novel approaches. Preprints 2020; 2020080641.
[89]
Oliveira GC, Varoto R, Cliquet A Jr. Brain tumor segmentation in magnetic resonance images using genetic algorithm clustering and adaboost classifier. Bioimaging 2018; 77-82.
[http://dx.doi.org/10.5220/0006534900770082]
[90]
Selvapandian A, Manivannan K. Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier. Int J Imaging Syst Technol 2018; 28(4): 295-301.
[http://dx.doi.org/10.1002/ima.22288]
[91]
Raja PS, Ramanan K. Lesion localization and extreme gradient boosting characterization with brain tumor MRI images.Advances in Data Science and Management. Singapore: Springer 2020; pp. 395-409.
[http://dx.doi.org/10.1007/978-981-15-0978-0_39]
[92]
Noreen N, Palaniappan S, Qayyum A, Ahmad I, Imran M, Shoaib M. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 2020; 8: 55135-44.
[http://dx.doi.org/10.1109/ACCESS.2020.2978629]
[93]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM 2017; 60(6): 84-90.
[http://dx.doi.org/10.1145/3065386]
[94]
Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images.Advances in Neural Information Processing Systems. Cambridge: The MIT Press 2012; pp. 2483-851.
[95]
Mohsen H, El-Dahshan ESA, El-Horbaty ESM, Salem ABM. Classification using deep learning neural networks for brain tumors. Future Comput Inform J 2018; 3(1): 68-71.
[http://dx.doi.org/10.1016/j.fcij.2017.12.001]
[96]
Anuse A, Vyas V. A novel training algorithm for convolutional neural network. Complex Intell Syst 2016; 2(3): 221-34.
[http://dx.doi.org/10.1007/s40747-016-0024-6]
[97]
Ge J, Sahiner B, Hadjiiski LM, et al. Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med Phys 2006; 33(8): 2975-88.
[http://dx.doi.org/10.1118/1.2211710] [PMID: 16964876]
[98]
Jiang J, Trundle P, Ren J. Medical image analysis with artificial neural networks. Comput Med Imaging Graph 2010; 34(8): 617-31.
[http://dx.doi.org/10.1016/j.compmedimag.2010.07.003] [PMID: 20713305]
[99]
Sharma M. Artificial neural network fuzzy inference system (ANFIS) for brain tumor detectio arXiv preprint 2012; arXiv:12120059.
[100]
Sharma M, Purohit GN, Mukherjee S. Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN).Networking Communication and Data Knowledge Engineering. Singapore: Springer 2018; pp. 145-57.
[http://dx.doi.org/10.1007/978-981-10-4600-1_14]
[101]
Arunkumar N, Mohammed MA, Abd Ghani MK, et al. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput 2019; 23(19): 9083-96.
[http://dx.doi.org/10.1007/s00500-018-3618-7]
[102]
Abdalla HEM, Esmail MY. Brain Tumor Detection by using Artificial Neural Network. International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). 1-6.
[http://dx.doi.org/10.1109/ICCCEEE.2018.8515763]
[103]
Amarapur B. Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier. Multimedia Tools Appl 2020; 79(5): 3571-99.
[104]
Dou Q, Yu L, Chen H, et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 2017; 41: 40-54.
[http://dx.doi.org/10.1016/j.media.2017.05.001] [PMID: 28526212]
[105]
Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognit Lett 2020; 131: 244-60.
[http://dx.doi.org/10.1016/j.patrec.2019.11.020]
[106]
Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: A review. J Med Syst 2018; 42(11): 226.
[http://dx.doi.org/10.1007/s10916-018-1088-1] [PMID: 30298337]
[107]
Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z. Deeply-supervised nets. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. 562-70.
[108]
Li H, Zhao R, Wang X. Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification. arXiv preprint 2014; arXiv:14124526.
[109]
Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data. arXiv preprint 2017; arXiv:170103056.
[110]
Pan Y, Huang W, Lin Z, et al. Brain tumor grading based on neural networks and convolutional neural networks. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 699-702.
[http://dx.doi.org/10.1109/EMBC.2015.7318458]
[111]
Hossain T, Shishir FS, Ashraf M, Al Nasim MA, Shah FM. Brain tumor detection using convolutional neural network. 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). 1-6.
[http://dx.doi.org/10.1109/ICASERT.2019.8934561]
[112]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[113]
Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017; 36: 61-78.
[http://dx.doi.org/10.1016/j.media.2016.10.004] [PMID: 27865153]
[114]
Isensee F, Kickingereder P, Bonekamp D, et al. Brain tumor segmentation using large receptive field deep convolutional neural networks.Bildverarbeitung für die Medizin. Berlin: Springer 2017.
[http://dx.doi.org/10.1007/978-3-662-54345-0_24]
[115]
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 2016; 35(5): 1252-61.
[http://dx.doi.org/10.1109/TMI.2016.2548501] [PMID: 27046893]
[116]
Rehman A, Khan MA, Saba T, Mehmood Z, Tariq U, Ayesha N. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc Res Tech 2021; 84(1): 133-49.
[http://dx.doi.org/10.1002/jemt.23597] [PMID: 32959422]
[117]
Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 2019; 111: 103345.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103345] [PMID: 31279167]
[118]
Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 2019; 30: 174-82.
[http://dx.doi.org/10.1016/j.jocs.2018.12.003]
[119]
Chen W, Liu B, Peng S, Sun J, Qiao X. S3D-UNet: separable 3D U-Net for brain tumor segmentation.Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. New York: Springer International Publishing 2019; pp. 358-68.
[http://dx.doi.org/10.1007/978-3-030-11726-9_32]
[120]
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, Eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer ScienceCham: Springer 2015.
[http://dx.doi.org/10.1007/978-3-319-24574-4_28]
[121]
Afshar P, Plataniotis KN, Mohammadi A. Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1386-72.
[http://dx.doi.org/10.1109/ICASSP.2019.8683759]
[122]
Xu F, Ma H, Sun J, Wu R, Liu X, Kong Y. LSTM Multi-modal UNet for Brain Tumor Segmentation. IEEE 4th International Conference on Image, Vision and Computing (ICIVC). 236-40.
[http://dx.doi.org/10.1109/ICIVC47709.2019.8981027]
[123]
Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: Cascaded framework for brain Tumour classification. IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). 633-7.
[http://dx.doi.org/10.1109/IECBES.2018.8626704]
[124]
Thapa S, Panday SP. Information and Communication Technology for Intelligent Systems. In: Senjyu T, Mahalle PN, Perumal T, Joshi A, Eds. Smart Innovation, Systems and Technologies, ICTIS. Singapore: Springer 2020; Vol. 196.
[125]
Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA. Brain tumor detection: A long short-term memory (LSTM)-based learning model. Neural Comput Appl 2020; 32(20): 15965-73.
[http://dx.doi.org/10.1007/s00521-019-04650-7]
[126]
Liu Y, Huang YX, Zhang X, et al. Deep C-LSTM neural network for epileptic seizure and tumor detection using high-dimension EEG signals. IEEE Access 2020; 8: 37495-504.
[http://dx.doi.org/10.1109/ACCESS.2020.2976156]
[127]
Han C, Rundo L, Araki R, et al. Infinite brain tumor images: Can GAN-based data augmentation improve tumor detection on MR Images? Proc Meeting on Image Recognition and Understanding (MIRU 2018). Sapporo, Japan. 2018.
[128]
Nema S, Dudhane A, Murala S, Naidu S. RescueNet: An unpaired GAN for brain tumor segmentation. Biomed Signal Process Control 2020; 55: 101641.
[http://dx.doi.org/10.1016/j.bspc.2019.101641]
[129]
Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 2020; 57: 101678.
[http://dx.doi.org/10.1016/j.bspc.2019.101678]

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