Magnetic resonance imaging (MRI) is a technological development in the medical field that produces images with high resolution to detect and then can classify diseases that are found in the organs of the patient's body. One condition that can be identified from reading an MRI image is a brain tumor. MRI technology is beneficial for medical for early detection of brain tumor disease. However, the weakness of detecting brain tumor disease with MRI images performed by doctors is still manual. It certainly requires a long process due to the complexity of the structure of the human brain. Of course, the slow process of detecting and classifying brain tumor disease in patients can cause delayed medical treatment for the patient's recovery. For this reason, based on the need for medical information needed by doctors to treat patients quickly and accurately, an image processing technique or method for reading MRI images is developed, the aim is to assist in processing medical images. In this research, we will review various techniques or methods that have been used to detect brain tumors on MRI images, and are expected to provide information on different techniques or methods in image processing as a basis for image processing MRI.
REFERENCES
1.
N.
Sciences
, “Survey of brain tumor detection techniques through MRI images
,” pp. 9
–13
, 2015
.2.
S. R.
Telrandhe
, A.
Pimpalkar
, and A.
Kendhe
, “Detection of brain tumor from MRI images by using segmentation & SVM
,” IEEE WCTFTR 2016 - Proc. 2016 World Conf. Futur. Trends Res. Innov. Soc. Welf.
, 2016
.3.
P.
Sharma
and J.
Suji
, “A Review on Image Segmentation with its Clustering Techniques
,” Int. J. Signal Process. Image Process. Pattern Recognit.
, vol. 9
, no. 5
, pp. 209
–218
, 2016
.4.
M.
Gurbina
, M.
Lascu
, and D.
Lascu
, “Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines
,” 2019 42nd Int. Conf. Telecommun. Signal Process. TSP 2019
, pp. 505
–508
, 2019
.5.
P. M. Sanjeev
Kumar
and S.
Chatteijee
, “Computer aided diagnostic for cancer detection using MRI images of brain (Brain tumor detection and classification system
),” 2016 IEEE Annu. India Conf. INDICON 2016
, 2017
.6.
H.
Fabelo
et al, “In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection
,” IEEE Access
, vol. 7
, no. c
, pp. 39098
–39116
, 2019
.7.
G.
Birare
and V. A.
Chakkarwar
, “Automated Detection of Brain Tumor Cells Using Support Vector Machine
,” 2018 9th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2018
, pp. 1
–4
, 2018
.8.
T.
Keerthana
and S.
Xavier
, “An Intelligent System for Early Assessment and Classification of Brain Tumor
,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018
, no. Icicct, pp. 1265
–1268
, 2018
.9.
S.
Harish
, G. F. A.
Ahammed
, and R.
Banu
, “An extensive research survey on brain MRI enhancement, segmentation and classification
,” Int. Conf. Electr. Electron. Commun. Comput. Technol. Optim. Tech. ICEECCOT 2017
, vol. 2018
-Janua, pp. 624
–631
, 2018
.10.
P. Kumar
Mallick
, S. H.
Ryu
, S. K.
Satapathy
, S.
Mishra
, G. N.
Nguyen
, and P.
Tiwari
, “Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network
,” IEEE Access
, vol. 7
, no. c
, pp. 46278
–46287
, 2019
.11.
N.
Kumari
and S.
Saxena
, “Review of Brain Tumor Segmentation and Classification
,” Proc. 2018 Int. Conf. Curr. Trends Towar. Converging Technol. ICCTCT 2018
, pp. 1
–6
, 2018
.12.
S.
Chauhan
, A.
More
, R.
Uikey
, P.
Malviya
, and A.
Moghe
, “Brain tumor detection and classification in MRI images using image and data mining
,” Int. Conf. Recent Innov. Signal Process. Embed. Syst. RISE 2017
, vol. 2018
-Janua, pp. 223
–231
, 2018
.13.
S.
Suja
, N.
George
, and A.
George
, “Classification of Grades of Astrocytoma Images from MRI Using Deep Neural Network
,” Proc. 2nd Int. Conf. Trends Electron. Informatics, ICOEI 2018
, no. Icoei, pp. 1257
–1262
, 2018
.14.
S. M.
Shelke
and S. W.
Mohod
, “Automated Segmentation and Detection of Brain Tumor from MRI
,” 2018 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2018
, pp. 2120
–2126
, 2018
.15.
G.
Shobana
and R.
Balakrishnan
, “Brain tumor diagnosis from MRI feature analysis - A comparative study
,” ICIIECS 2015 - 2015 IEEE Int. Conf. Innov. Information, Embed. Commun. Syst.
, pp. 0
–3
, 2015
.16.
et al B.B
Shankaragowda
, “A Novel Approach For The Brain Tumor Detection and Classification Using Support Vector Machine
,” pp. 90
–93
, 2017
.17.
C.
Saha
and M. F.
Hossain
, “MRI brain tumor images classification using K-means clustering, NSCT and SVM
,” 2017 4th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Comput. Electron. UPCON 2017
, vol. 2018
- Janua, pp. 329
–333
, 2017
.18.
M. H. O.
Rashid
, M. A.
Mamun
, M. A.
Hossain
, and M. P.
Uddin
, “Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images
,” Int. Conf. Comput. Commun. Chem. Mater. Electron. Eng. IC4ME2 2018
, pp. 3
–6
, 2018
.19.
A. Reema
Matthew
, A.
Prasad
, and P. Babu
Anto
, “A review on feature extraction techniques for tumor detection and classification from brain MRI
,” 2017 Int. Conf. Intell. Comput. Instrum. Control Technol. ICICICT 2017
, vol. 2018
-Janua, pp. 1766
–1771
, 2018
.20.
S.
Saeed
, A.
Shaikh
, M. A.
Memon
, and S. M. R.
Naqvi
, “Technique for Tumor Detection Upon Brain MRI Image by Utilizing Support Vector Machine
,” Quest Res. J.
, vol. 16
, no. 1
, pp. 36
–40
, 2018
.21.
J. G. and
H.
Inbarani
H., “Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification
,” Appl. Soft Comput. J.
, vol. 46
, pp. 639
–651
, 2016
.22.
M. M.
Subashini
, S. K.
Sahoo
, V.
Sunil
, and S.
Easwaran
, “A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques
,” Expert Syst. Appl.
, vol. 43
, pp. 186
–196
, 2016
.23.
P.
Kumar
and B. Vijay
Kumar
, “Brain tumor MRI segmentation and classification using ensemble classifier
,” Int. J. Recent Technol. Eng.
, vol. 8
, no. 1
Special Issue 4, pp. 244
–252
, 2019
.24.
R. K.
Garg
, A.
Kulshreshtha
, and M. T.
Scholar
, “A Review of Automated MRI Image Processing Techniques Employing Segmentation & Classification
,” Int. J. Comput. Sci. Trends Technol.
, vol. 5
, no. 2
, pp. 117
–120
, 2013
.25.
N. B.
Bahadure
, A. K.
Ray
, and H. P.
Thethi
, “Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM
,” Int. J. Biomed. Imaging
, vol. 2017
, 2017
.26.
P.
Shanthakumar
and P. Ganesh
Kumar
, “Computer aided brain tumor detection system using watershed segmentation techniques
,” Int. J. Imaging Syst. Technol.
, vol. 25
, no. 4
, pp. 297
–301
, 2015
.
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