Texture Feature Analysis and Light Gradient Boosting Machine for Accurate Brain Tumor Detection in MRI Scans
Keywords:
Brain Tumor Classification; Classification Accuracy Light; Gradient Boosting Machine (LGBM); Gray Level Co-occurrence Matrix (GLCM); MRI; Machine LearningAbstract
This article proposes a method for classifying brain tumors using Light Gradient Boosting Machine (LGBM) technology based on MRI scans. A publicly available dataset of 253 MRI images (98 normal, 155 abnormal) sourced from Kaggle was utilized for validation. Gray Level Co-occurrence Matrix (GLCM) features were extracted to capture image textures and characterize potential tumor presence. The data was divided into training and testing sets, and image pre-processing techniques like filtering and thresholding were applied to enhance image quality. The proposed LGBM model achieved an accuracy of 91.70%, outperforming an existing Convolutional Neural Network (CNN) framework by 19.37%. Confusion matrix analysis further confirmed the effectiveness of the LGBM model in accurately classifying brain tumors. This study demonstrates the potential of LGBM as a powerful tool for brain tumor classification in MRI analysis, contributing to the advancement of medical image analysis and diagnosis.
Downloads
References
Hemanth, D.J., Anitha, J., Naaji, A., Geman, O., Popescu, D.E.: A modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7, 4275– 4283 (2019)
E.-S. A. El-Dahshan, H. M. Mohsen, K. Revett, and A.-B. M. Salem, “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm,” Expert Systems with Applications, vol. 41, no. 11, pp. 5526–5545, 2014
Agrawal, P., Chourasia, V., Kapoor, R., Agrawal, S.: A Comprehensive study of the image enhancement techniques. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1, 85–89 (2014)
Liu, J., Guo, L.: A new brain MRI image segmentation strategy based on k-means clustering and SVM. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 270–273. IEEE (2015)
Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R: Automated segmentation of MR images of brain tumors. Radiology 218:586–591, 2001
P. Katti, V. R. Marathe, “Implementation of Classification System for Brain Tumor using Probabilistic Neural Network,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, no. 10, pp. 188-192, October 2015.
Mzoughi H, Njeh I, Wali A, Slima MB, BenHamida A, Mhiri C, Mahfoudhe KB. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. J Digit Imaging. 2020 Aug;33(4):903-915. doi: 10.1007/s10278-020-00347-9. PMID: 32440926; PMCID: PMC7522155.
Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016).
C. L. Devasena and M. Hemalatha, “Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm,” Central European Journal of Computer Science, vol. 3, no. 3, pp. 117–128, 2013.
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35, 5, 1240–1251, (2016).
Cui S, Mao L, Jiang J, Liu C, Xiong S. Automatic semantic segmentation of brain Gliomas from MRI images using a deep cascaded neural network. J Healthc Eng. 2018;2018(4940593):1-14.
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)
A. Batra, Dr. G. Kaushik, “SECTUBIM: Automatic Segmentation And Classification of Tumeric Brain MRI Images using FHS (FCM, HWT and SVM),” International Journal of Engineering Science and Computing, Vol. 7, no.6, pp. 13190-13194, June 2017.
Muhammad Sajjada, Salman Khanb, Khan Muhammad,Wanqing Wu, Amin Ullah, Sung Wook Baik ,” Multi-grade brain tumor classification using deep CNN with extensive data augmentation”, 1877-7503, Journal of Computational Science-Elsevier,30,174- 182(2019)
Pan Y, Huang W, Lin Z, Zhu W, Zhou J, Wong J. Brain tumor grading based on neural networks and convolutional neural networks. Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, pp. 699–702, 2015.
Khawaldeh, Saed, et al. Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Applied Sciences, Vol 8, 2017.
Chaddad A. Automated feature extraction in brain tumor by magnetic resonance imaging using Gaussian mixture models. Int J Biomed Imag. 2015;2015(868031):1-11
Javaria Amin, Muhammad Sharif , Mudassar Raza , Tanzila Saba , Muhamma Almas Anjum,” Brain tumor detection using statistical and machine learning method,” Computer Methods and Programs in Biomedicine-Elsevier ,177,69-79(2019).
M. B. M. Amien, A. Abd-elrehman, and W. Ibrahim, “An intelligent model for automatic brain-tumor diagnosis basedon MRI images,” International Journal of Computer Applications, vol. 72, no. 23, pp. 21–24, June 2013
Amin Kabir Anaraki ,Moosa Ayati ,Foad Kazemi, Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms,(2019),volume 39,63-74, https://doi.org/10.1016/j.bbe.2018.10.004
L. A. Montejo and L. E. Suárez, “Aplicaciones de la Transformada Ondícula (“Wavelet”) en Ingeniería Estructural,” Mecanica Computacional, vol. 26, pp. 2742–2753, 2007.
J. Cheng, X. Chen, H. Yang, and M. Leng, “An enhanced k- means algorithm using agglomerative hierarchical clustering strategy,” International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). pp. 407–410, 2012.
Ayse Demirhan, Mustafa Toru, Inan Guler, “Segmmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks”, IEEE Journal of Biomedical and Health Informatics, vol.19,no.4,pp.1451-1458,July 2015.
Ch.Amulya, G.Prathibha, “ MRI Brain Tumor Classification Using SURF and SIFT Features”, International Journal for Modern Trends in Science and Technology,vol.2, Issue.7,ISSN:2455-3778,pp.123-127,July 2016
Atiq Islam, Syedd M S Reza and Khan M Iftekharuddin, “ Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors”, IEEE Trans on Biomedical Engg, Vol 60, No.11,pp. 3204-3215, Nov 2013.
Evangelia I Zacharaki, Sumei Wang,Sanjeev Chawla, Dong Soo Yoo, Ronald Wolf, Elias R Melhem and Christos Davatzikos, “Classification of Brain Tumor Type and Grade using MRI Texture and shape in a Machine Learning Scheme”, Magnetic Resonance in Medicine, vol.62,no.6, pp.1609-1618,2009.
S. Ruder, “An overview of gradient descent optimization algorithms,” pp. 1–14, 2016, [Online]. Available: http://arxiv.org/abs/1609.04747.
G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, 2017, pp. 1–9.
S.Perumal et.al. “Preprocessing by Contrast Enhancement Techniques for Medical Images” International Journal of Pure and Applied Mathematics Volume 118 No. 18 2018, 3681-3688 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version).
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 2016, pp. 785–794.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
