An effective method for the identification of multi-class tumors in brain magnetic resonance imaging
DOI:
https://doi.org/10.22399/ijcesen.841Keywords:
Gray Level Co-occurrence Matrix, Feature reduction, Maximum Difference Feature Selection, Classification - SVM, KNNAbstract
The field of medicine makes extensive use of image classification, which is one of the computational applications that is specifically used for the purpose of identifying anomalies in magnetic resonance (MR) brain pictures. Classification, feature extraction, and feature reduction are the three components that make up the head tumor classification method that has been suggested. The Gray Level Co-occurrence Matrix (GLCM) is used in the process of feature extraction. The Maximum Difference Feature Selection (MDFS) approach is used for the purpose of feature selection within the context of reducing the coefficient of the picture. During the classification process, K Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers are used to categorize the pictures. These classifiers are trained using the extracted features provided before. The performance of feature extraction techniques using two different classifiers is compared in terms of assessment metrics, sensitivity, specificity, and accuracy. This comparison is based on the outcomes of the experiments. We are able to draw the conclusion that the combination of Gray Level Co-occurrence Matrix and Maximum Difference Feature Selection with Support Vector Machines demonstrates an accuracy of 95.0% based on the results of the comparison
References
Saritha.M,Paul Joseph.K, Abraham Mathew.T, (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network Pattern Recognition Letters . 34(16);2151-2156. https://doi.org/10.1016/j.patrec.2013.08.017
Chen Gang, Chen Ning, Lin Xia, (2013). The Image Retrieval Based on Scale and Rotation-Invariant Texture Features of Gabor Wavelet Transform, IEEE Fourth World Congress on Software Engineering.
Dharmendra Patidar, Bhavin C. Shah, Manoj R. Mishra “Performance Analysis of K Nearest Neighbors Image Classifier with Different Wavelet features, International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014.
El-Sayed Ahmed El-Dahshan, Tamer Hosny and Abdel-Badeeh M. Salem (2010). Hybrid intelligent techniques for MRI brain images classification, Digital signal processing, 20;433441, 2010.
Guang-Hai Liu, LeiZhang, Ying-KunHou and Zuo-YongLi (2010). Image retrieval based on multi-texton histogram. Pattern recognition, 43;2380–2389.
Hari Babu Nandpuru, Dr. S. S. Salankar, Prof. V. R. Bora (2014). MRI Brain Cancer Classification Using Support Vector Machine, IEEE Students' Conference on Electrical, Electronics and Computer Science.
Jayachandran .A and Kharmega Sundararaj .G (2015). Abnormality Segmentation and Classification of Multi class Brain Tumor in MR Images using fuzzy logic based hybrid kernel SVM, International Journal of Fuzzy Systems, 17, 434–443 https://doi.org/10.1007/s40815-015-0064-x
Jayachandran .A and Dhanasekaran .R (2013). “Automatic Detection of Brain Tumor in Magnetic Resonance Images using Multi-Texton Histogram and Support Vector Machine, Wiley Periodicals, 23 https://doi.org/10.1002/ima.22041
Jin Liu, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu and Yi Pan (2014). A Survey of MRI based brain tumor segmentation methods, Tsinghua Science and Technology 19;578–595.
Jayachandran .A and Dhanasekaran .R (2014) Brain Tumor Severity Analysis Using Modified Multi-Texton Histogram and Hybrid Kernel SVM, Wiley Periodicals, 24;72-82.
Noramalina Abdullah, Umi Kalthum Ngah, Shalihatun Azlin Aziz (2011). Image Classification of Brain MRI Using Support Vector Machine”, IEEE. DOI:10.1109/IST.2011.5962185
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6), 610-621.
Zacharaki, E. I., Wang, S., Chawla, S., Yoo, D. S., Wolf, R., & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine, 62(6), 1609-1618.
Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1-3), 131-159.
Menze, B. H., Jakab, A., Bauer, S., et al. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993-2024.
Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2(1), 315-337.
Raut, B., Joshi, A., & Gupta, A. (2018). Feature extraction techniques for image classification: A survey. Pattern Recognition Letters, 30(6), 891-904.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Verma, R., Chouhan, S. S., & Singh, S. (2020). Multi-class brain tumor classification using improved texture and shape features. Biomedical Signal Processing and Control, 57, 101736.
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240-1251.
Rehman, A., Abbas, N., Saba, T., & Mehmood, Z. (2020). Deep learning-based brain tumor classification. Neural Computing and Applications, 32(8), 2293-2304.
Tustison, N. J., Avants, B. B., Cook, P. A., et al. (2014). N4ITK: improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320.
Rajendran, M., Shenbagavalli, V., & Jeyaprakash, S. (2021). Hybrid approach for brain tumor classification using handcrafted and deep learning-based features. Journal of Medical Systems, 45(8), 1-15.
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