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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

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

Author(s): Aaishwarya Sanjay Bajaj* and Usha Chouhan

Volume 16, Issue 8, 2020

Page: [937 - 945] Pages: 9

DOI: 10.2174/1573405615666190903144419

Price: $65

Abstract

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection.

Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research.

Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.

Graphical Abstract

[1]
Tagare HD, Jaffe CC, Duncan J. Medical image databases: a content-based retrieval approach. J Am Med Inform Assoc 1997; 4(3): 184-98.
[http://dx.doi.org/10.1136/jamia.1997.0040184 ] [PMID: 9147338]
[2]
Borole VY, Nimbhore SS, Kawthekar DS. image processing techniques for brain tumor detection: a review. Int J Emerg Trends Technol Comput Sci 2015; 4(5): 2. [IJETTCS]
[3]
Bobbillapati S, Rani AJ. Automatic detection of brain tumor through a magnetic resonance image. Int J Sci Res Publ 2014; 3(11): 1-5.
[4]
Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q. Brain tumor segmentation based on local independent projection-based classification. IEEE Trans Biomed Eng 2014; 61(10): 2633-45.
[http://dx.doi.org/10.1109/TBME.2014.2325410 ] [PMID: 24860022]
[5]
Tayade RG, Patil MP, Sonawane MP. A review on various techniques of brain tumor detection. Int J Comput Sci Trends Technol 2016; 4(2): 19-82. [IJCSTT]
[6]
Rani K. A study of various brain tumor detection techniques. Int J Comput Technol Appl 2015; 6(3): 459-67.
[7]
Kapse RS, Salankar SS, Babar M. Literature survey on detection of brain tumor from MRI images. J Electron Commun Eng. 2015; (1): 80-6. [IOSRJECE]
[8]
Vishnumurthy TD, Mohana HS, Meshram VA, Kammar P. Suppression of herringbone artifact in MR images of the brain using combined wavelet and FFT based filtering technique. Int J Comput Sci Eng 2016; 4(2): 66-71.
[9]
Nikam SS. A comparative study of classification techniques in data mining algorithms. Orient J Comp Sci Technol 2015; 8(1): 13-9.
[10]
Seetha M, Muralikrishna IV, Deekshatulu BL, Malleswari BL, Hegde P. Artificial neural networks and other methods of image classification. J Theor Appl Inform Technol 2008; 4(11)
[11]
Saravanan K, Sasithra S. Review on classification based on artificial neural networks. Int J Ambient Syst Appl 2014; 2(4): 11-8. [IJASA]
[http://dx.doi.org/10.5121/ijasa.2014.2402]
[12]
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007; 114(2): 97-109.
[http://dx.doi.org/10.1007/s00401-007-0243-4 ] [PMID: 17618441]
[13]
Kleihues P, Burger PC, Scheithauer BW. The new WHO classification of brain tumours. Brain Pathol 1993; 3(3): 255-68.
[http://dx.doi.org/10.1111/j.1750-3639.1993.tb00752.x ] [PMID: 8293185]
[14]
Selvathi D, Anitha J. Effective fuzzy clustering algorithm for abnormal MR brain image segmentation. In: 2009 IEEE International Advance Computing Conference; 2009 Mar 6; 609; Patiala, India IEEE.
[15]
Charutha S, Jayashree MJ. An efficient brain tumor detection by integrating modified texture-based region growing and cellular automata edge detection. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) 2014; 1193-9.Kanyakumari, India. IEEE.
[http://dx.doi.org/10.1109/ICCICCT.2014.6993142]
[16]
Abdullah AA, Chize BS, Nishio Y. Implementation of an improved cellular neural network algorithm for brain tumor detection. 2012 International Conference on Biomedical Engineering (ICoBE) 2012; 611-5; Penang, Malaysia. IEEE.
[http://dx.doi.org/10.1109/ICoBE.2012.6178990]
[17]
Maiti I, Chakraborty M. A new method for brain tumor segmentation based on watershed and edge detection algorithms in the HSV color model.2012; Durgapur, India .IEEE
[18]
Preetha R, Suresh GR. Performance analysis of fuzzy c means algorithm in automated detection of brain tumors 2014.
[http://dx.doi.org/10.1109/WCCCT.2014.26]
[19]
Sharma B, Singh BM. Review paper on brain tumor detection using pattern recognition techniques. Int J Recent Res Asp 2016; 3(2): 151-6.
[20]
Ribeiro MX, Traina AJ, Traina C, Azevedo-Marques PM. An association rule-based method to support medical image diagnosis with efficiency. IEEE Trans Multimed 2008; 10(2): 277-85.
[http://dx.doi.org/10.1109/TMM.2007.911837]
[21]
Tahir MN. Classification and characterization of brain tumor MRI by using grayscaled segmentation and DNN 2018.
[22]
Pandey ON, Jogi SP, Yadav S, Arjun V, Kumar V. Review on brain tumor detection using digital image processing. Int J Sci Eng Res 2014; 5(5): 1351-5.
[23]
Jellinger KA. Eur J Neurol 2009; 16(7)e136
[http://dx.doi.org/10.1111/j.1468-1331.2009.02678.x ]
[24]
www.cancer.ca/~/media/CCE 10/08/2015
[25]
Novelline RA, Squire LF. Squire’s fundamentals of radiology La Editorial 2004.
[26]
Gupta S, Chauhan RC, Saxena SC. Homomorphic wavelet thresholding technique for denoising medical ultrasound images. J Med Eng Technol 2005; 29(5): 208-14.
[http://dx.doi.org/10.1080/03091900412331286396 ] [PMID: 16126580]
[27]
Donoho DL. De-noising by soft-thresholding. IEEE Trans Inf Theory 1995; 41(3): 613-27.
[http://dx.doi.org/10.1109/18.382009]
[28]
Gupta S, Chauhan RC, Saxena SC. Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Med Biol Eng Comput 2005; 43(2): 189-95.
[http://dx.doi.org/10.1007/BF02345953 ] [PMID: 15865126]
[29]
Gupta S, Chauhan RC, Saxena SC. Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using speckle modeling based on Rayleigh distribution. IEE Proc Vis Image Signal Process 2005; 152(1): 129-35.
[http://dx.doi.org/10.1049/ip-vis:20050975]
[30]
Gupta S, Kaur L, Chauhan RC, Saxena SC. A wavelet-based statistical approach for speckle reduction in medical ultrasound images. A wavelet-based statistical approach for speckle reduction in medical ultrasound images 2003. Conference on Convergent Technologies for Asia-Pacific Region 2003.
[http://dx.doi.org/10.1109/TENCON.2003.1273218]
[31]
Gupta S, Kaur L, Chauhan RC, Saxena SC. A versatile technique for visual enhancement of medical ultrasound images. Digit Signal Process 2007; 17(3): 542-60.
[http://dx.doi.org/10.1016/j.dsp.2006.12.001]
[32]
Gupta S, Chauhan RC, Sexana SC. Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med Biol Eng Comput 2004; 42(2): 189-92.
[http://dx.doi.org/10.1007/BF02344630 ] [PMID: 15125148]
[33]
Kamble VM, Parlewar P, Keskar AG, Bhurchandi KM. Performance evaluation of wavelet, ridgelet, curvelet, and contourlet transforms based techniques for digital image denoising. Artif Intell Rev 2016; 45(4): 509-33.
[http://dx.doi.org/10.1007/s10462-015-9453-7]
[34]
Kaur L, Gupta S, Chauhan RC, Saxena SC. Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets. Digit Signal Process 2007; 17(1): 189-98.
[http://dx.doi.org/10.1016/j.dsp.2006.05.008 ]
[35]
Kaur P, Singh G, Kaur P. A review of denoising medical images using machine learning approaches. Curr Med Imaging Rev 2018; 14(5): 675-85.
[http://dx.doi.org/10.2174/1573405613666170428154156 ] [PMID: 30532667]
[36]
Bhardwaj A, Siddhu KK. An approach to medical image classification using Neuro-fuzzy logic and Anfis classifier. Int J Comput Trends Tech 2013; 4(3): 236-40.
[37]
Roy S, Sadhu S, Bandyopadhyay SK, Bhattacharyya D, Kim TH. Brain tumor classification using an adaptive neuro-fuzzy inference system from MRI. Int J Bio-Sci Bio-Technol 2016; 8(3): 203-18.
[http://dx.doi.org/10.14257/ijbsbt.2016.8.3.21]
[38]
Mathew ST, Nachamai M. Brain tumor detection from human brain magnetic resonance images using image mining technique. Int J Comput Intel Res 2017; 13(10): 2341-55.
[39]
Parvathavardhini S, Manju S. Cancer gene detection using neuro-fuzzy classification algorithm. Int J Sci Res Comp Sci. Eng Info Technol 2018; 3(3): 1223-9.
[40]
Harish B, Rokade P, Kadam A, Nanaware S. Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of image ROI and Retrieval of ROI based on MP-KDD. Int Res J Eng Technol 2017; 4(3): 832-8.

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