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Current Cancer Therapy Reviews

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ISSN (Print): 1573-3947
ISSN (Online): 1875-6301

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Research Article

An In Silico Approach for Brain Tumor Detection and Classification of Magnetic Resonance Images

Author(s): Ashfaq Hussain* and Afzal Hussain

Volume 18, Issue 3, 2022

Published on: 21 July, 2022

Page: [209 - 214] Pages: 6

DOI: 10.2174/1573394718666220329184137

Price: $65

Abstract

Background: Early detection of cancer can be done using machine learning approaches with high precision. A brain tumor is a very dangerous disease that may cause the death of cancerous patients. Every year, thousands of people die from that disease all over the world. Proper detection of cancerous cells in the body can save their lives.

Methods: To segment the brain tumor region through brain MR images and to classify tumorous and normal brain images into different classes is very crucial to cure death-causing diseases like cancer. There are various techniques or methods for segmenting the tumorous part or area from the medical images. Magnetic resonance imaging is the most important technique to capture the images of the body parts because it has more information than any other imaging method, such as a CT scan, etc. K-means clustering is used for the segmentation of the tumor region, and the SVM classifier is used for classification purposes.

Results: The classification was done through the support vector machines in MATLAB 2019a. 350 images were classified with an accuracy of 89.7 %.

Conclusion: In this paper, MRI images have been used for tumor detection and classification of those images into different classes with the help of MATLAB software. We calculated the accuracy of the classification using machine learning techniques. Early detection of cancerous regions is effective in curing death-causing diseases.

Keywords: Magnetic resonance Images, machine learning, brain tumor detection, SVM classifier, MATLAB, cancer

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[1]
John P. Brain tumor classification using wavelet and texture based neural network. Int J Sci Eng Res 2012; 3(10): 1-7.
[2]
Gonzalez RC, Woods R. Digital image processing: Pearson education India. 2009.
[3]
Devkota B, Alsadoon A, Prasad P, Singh A, 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]
[4]
Halalli B, Makandar A. Computer aided diagnosis-medical image analysis techniques. Breast Imaging. 2018; p. 85.
[5]
Joon P, Bajaj SB, Jatain A. Segmentation and detection of lung cancer using image processing and clustering techniques Progress in advanced computing and intelligent engineering. Springer 2019; pp. 13-23.
[6]
Saikumar K, Rajesh V, Ramya N, Ahammad SH, Kumar GNS. A deep learning process for spine and heart segmentation using pixel-based convolutional networks. J Int Pharm Res 2019; 46(1): 278-82.
[7]
Cai L, Gao J, Zhao D. A review of the application of deep learning in medical image classification and segmentation. Ann Transl Med 2020; 8(11): 713.
[http://dx.doi.org/10.21037/atm.2020.02.44] [PMID: 32617333]
[8]
Dhillon A, Verma GK. Convolutional neural network: A review of models, methodologies and applications to object detection. Progress in Artificial Intelligence 2020; 9(2): 85-112.
[http://dx.doi.org/10.1007/s13748-019-00203-0]
[9]
Pak M, Kim S, Eds. A review of deep learning in image recognition. 2017 4th international conference on computer applications and information processing technology (CAIPT). IEEE. 2017.
[10]
Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM. 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]
[11]
Tang TT, Zawaski JA, Francis KN, Qutub AA, Gaber MW. Image-based classification of tumor type and growth rate using machine learning: A preclinical study. Sci Rep 2019; 9(1): 12529.
[http://dx.doi.org/10.1038/s41598-019-48738-5] [PMID: 31467303]
[12]
MR image classification using adaboost for brain tumor type. 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE. 2017.
[13]
Groza V, Tuchinov B, Pavlovskiy E, Amelina E, Amelin M, Golushko S, Eds. Data preprocessing via multi-sequences MRI mixture to improve brain tumor segmentation international work-conference on bioinformatics and biomedical engineering. Springer 2020; pp. 695-704.
[14]
Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit Lett 2020; 129: 181-9.
[http://dx.doi.org/10.1016/j.patrec.2019.11.019]
[15]
Hamad YA, Simonov KV, Naeem MB. Detection of brain tumor in MRI images, using a combination of fuzzy C-means and thresholding. Int J Adv Pervasive Ubiquitous Comput 2019; 11(1): 45-60.
[http://dx.doi.org/10.4018/IJAPUC.2019010104]
[16]
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]
[17]
Jafarpour S, Sedghi Z, Amirani MC. A robust brain MRI classification with GLCM features. Int J Comput Appl 2012; 37(12): 1-5.
[18]
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.
[19]
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; 9749108.
[http://dx.doi.org/10.1155/2017/9749108]
[20]
Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 2021; 1-11.
[21]
Hussain A, Khunteta A, Eds. Semantic segmentation of brain tumor from MRI images and SVM classification using GLCM features 2020. Second International Conference on Inventive Research in Computing Applications (ICIRCA).
[http://dx.doi.org/10.1109/ICIRCA48905.2020.9183385]

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