IJBTT

Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection

Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection
	© 2022 by IJETT Journal
	Volume-70 Issue-2
	Year of Publication : 2022
	Authors : Ibrahima Sory keita, Ir.Pratap Nair, Haarindra Prasad, Sudhakara pandian, S.Deivasigamani
	DOI :  10.14445/22315381/IJETT-V70I2P226

How to Cite?

Ibrahima Sory keita, Ir.Pratap Nair, Haarindra Prasad, Sudhakara pandian, S.Deivasigamani, "Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 234-240, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P226

Abstract
A brain tumor is a clump of malformed tissue in which the cells proliferate rapidly and uncontrollably. Differentiating brain tumors from other brain tissue is critical for clinical diagnosis and therapy methods. This article presented a method for detecting and classifying brain tumor cells using a machine learning algorithm based on the Discrete Wavelet Transform (DWT). Additionally, characteristics from 2D DWT components are retrieved for the categorization of Benign (Be) and Malignant (Ma) Magnetic resonance imaging (MRI). After that, the features are trained and classified using the kernel Support Vector Machine (SVM) classification method. The proposed method stated in this work obtains 99% of accuracy (ACC), 99.14% of sensitivity (Se), and 98.79 % of specificity (Sp) concerning the MRI ground truth images.

Keywords
Benign and malignant, Brain tumor, DWT, MR images, Neural networks, SVM.

Reference
[1] Iqbal S, Khan MUG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett. 8(1) (2018) 5–28.
[2] Sahoo L, Sarangi L, Dash BR, Palo HK. Detection and Classification of Brain Tumor Using Magnetic Resonance Images. Lect Notes Electr Eng. 665(1) (2020)429–41.
[3] Song G, Huang Z, Zhao Y, Zhao X, Liu Y, Bao M, et al. A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM. IEEE Access.7 (2019)13842–55.
[4] Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magn Reson Imaging [Internet].61 (2018) 300–18. Available from: https://doi.org/10.1016/j.mri.2019.05.028
[5] Naz S, Majeed H, Irshad H. Image segmentation using fuzzy clustering: A survey. Proc - 2010 6th Int Conf Emerg Technol ICET. (2010) 181–6.
[6] Kang J, Ullah Z, Gwak J. Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors. 21(6) (2021)1–21.
[7] Hemanth G, Janardhan M, Sujihelen L. Design and implementing brain tumor detection using machine learning approach. Proc Int Conf Trends Electron Informatics, ICOEI . (2019) 1289–94.
[8] Banerjee S, Mitra S, Masulli F, Rovetta S. Brain tumor detection and classification from multi-sequence MRI: study using convnets. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 11383 (2019)170–9.
[9] Bahadure NB, Ray AK, Thethi HP. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. Int J Biomed Imaging.(2017).
[10] Reema Mathew A, Babu Anto P, Thara NK. Brain tumor segmentation and classification using DWT, Gabour wavelet and GLCM. 2017 Int Conf Intell Comput Instrum Control Technol ICICICT .(2018) 1744–50.
[11] Amin J, Sharif M, Yasmin M, Fernandes SL. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognit Lett [Internet]. 139 (2020) 118–27. Available from: https://doi.org/10.1016/j.patrec.2017.10.036
[12] 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.61(10) (2014) 2633–45.
[13] Faisal Z, El Abbadi NK. Detection and recognition of brain tumor based on DWT, PCA and ANN. Indones J Electr Eng Comput Sci.18(1) (2019) 56–63.
[14] Makandar A, Halalli B. Image Enhancement Techniques using Highpass and Lowpass Filters. Int J Comput Appl. 109(14) (2015) 21–7.
[15] Zulpe N, Pawar V. GLCM textural features for Brain Tumor Classification. Int J Comput Sci Issues. 9(3) (2012) 354–9.
[16] Shingare K V, N. D. P. An Efficient Brain Image Classification Using Probabilistic Neural Network and Tumor Detection Using Image Processing. IJARCCE. 4(5) (2014)631–8.
[17] Anuja SB, K UN, Sukanya ST. ECG Signals Classification using Statistical and Wavelet Features. Int J Recent Technol Eng. 8(5) (2020)1497–504.
[18] Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl.24(5) (2014) 1163–77.
[19] El Kader IA, Xu G, Shuai Z, Saminu S. Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images. Curr Med Imaging Former Curr Med Imaging Rev. (2021) 17.
[20] Nabizadeh N, Kubat M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs statistical features. Comput Electr Eng [Internet]. 45 (2015) 286–301. Available from: http://dx.doi.org/10.1016/j.compeleceng.2015.02.007
[21] Zhang Y et al. Progress In Electromagnetics Research, 130 (2012) 369–388, 2012. Prog Electromagn.
[22] Agarap AFM. A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. ACM Int Conf Proceeding Ser. (2006) (2018) 26–30.