Classification of Brain Tumors based on Coherence-based Atoms Correction in Overcomplete Models Learning

Subject Areas : Majlesi Journal of Telecommunication Devices

Samira Mavaddati ¹ , Hamidreza Koohi ² , Ziad Kobti ³

1 - Department of Engineering and Technology, University of Mazandaran, Babolsar, Iran
2 - School of Computer Science, University of Windsor, Ontario, Canada
3 - School of Computer Science, University of Windsor, Ontario, Canada

Received: 2024-01-28 Accepted : 2024-07-14 Published : 2024-11-10

Keywords: Brain Tumor Classification, Dictionary Learning, Sparse Representation, ResNet Deep Model, Sparse Non-negative Matrix Factorization.,

Abstract :

Brain tumor detection using MRI imaging has the potential to be greatly improved through the integration of medical knowledge. Solving the problem of brain tumor classification is highly important in the field of medicine as it can greatly impact the effectiveness of treatment options. However, the classification of tumors into Benign or Malignant categories remains a challenging task due to the need for detailed texture analysis and the possibility of errors. Image processing techniques such as dictionary learning-based classifiers can play a critical role in this field. This paper proposes a method that combines textural-statistical features to categorize brain tumors based on employing sparse non-negative matrix factorization (SNMF) and a dictionary learning-based model using a sparse representation technique. In the next step, the extracted features from the sparse coefficient matrix were fed into a ResNet10 model for the classification of the input image. The experimental results emphasize that the proposed method, which trains the dictionary atom based on the combinational features vector, can accurately distinguish different types of brain tumors with high precision. This is a significant method as it can improve the effectiveness of brain tumor classification, leading to more accurate treatment decisions for patients.

References:

[1] H. Kibriya, R. Amin, J. Kim, M. Nawaz, and R. Gantassi, “A novel approach for brain tumor classification using an ensemble of deep and hand-crafted features,” Sensors, vol. 23, 2023.
[2] J. Amin, M. Sharif, A. Haldorai, and et al. “Brain tumor detection and classification using machine learning: a comprehensive survey,” Complex Intell. Syst. 8,p. 316183, 2022. https://doi.org/10.1007/s40747-021-00563-y.
[3] E. U. Haq, H. Jianjun, X. Huarong, K. Li, and L. Weng, “A hybrid approach based on deep CNN and machine learning classifiers for the tumor segmentation and classification in brain MRI,” Computational and mathematical methods in medicine, vol. 2022, Article ID 6446680, 2022. https://doi.org/10.1155/2022/6446680.
[4] J. Pauline, “Brain tumor classification using wavelet and texture based neural network,” International Journal of Scientific & Engineering Research, vol. 3, no. 10, 2012.
[5] N. Zulpe, and V. Pawar, “GLCM textural features for brain tumor classification,” IJCSI International Journal of Computer Science Issues, vol. 9, pp. 3549, 2012.
[6] S. Jain, “Brain cancer classification using GLCM based feature extraction in artificial neural network,” International Journal of Computer Science & Engineering Technology, vol. 4, no. 7, pp. 966-970, 2013.
[7] G. Preethi, and V. Sornagopal, “MRI image classification using GLCM texture features,” International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, pp. 1-6, 2014.
[8] N.V. Chavan, B.D. Jadhav, and P.M. Patil, “Detection and classification of brain tumors,” International Journal of Computer Applications, vol. 112, no. 8, 2015.
[9] P. Udayabhanu, V. Anjaly, and S. Padmarajan, “MRI brain image classification using GLCM feature extraction and probabilistic neural networks,” IOSR Journal of Electronics and Communication Engineering, pp. 4-15, 2016.
[10] S. Lekshmi, and I. Ratheesh, “Classification of brain tumor using neural network,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 4, no. 8, pp. 93-97, 2016.
[11] B. Thamaraichelvi, and G. Yamuna, “Gray level co- occurrence matrix features based classification of tumor in medical images,” ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 19, pp. 11403-11414, 2016.
[12] A. Harshavardha, S. Babu, and T. Venugopal, “Analysis of feature extraction methods for the classification of brain tumor detection,” International Journal of Pure and Applied Mathematics, vol. 117, no. 7, pp. 147-155.
[13] N. Varuna, and T.N.R. Kumar, “Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network,” Brain Informatics, pp. 23-30, 2017.
[14] L. Kapoor, “A survey on brain tumor detection using image processing techniques,” 7th International Conference on Cloud Computing, Data Science & Engineering, pp. 582-585, 2017.
[15] S. Kumar, and V.S. Dharun, “Extraction of texture features using GLCM and shape features using connected regions,” International Journal of Engineering and Technology (IJET), pp. 2926-2930, 2017.
[16] M. Nasir, A. Baig, and A. Khanum, “Brain tumor classification in MRI scans using sparse representation,” In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP. Lecture Notes in Computer Science, vol.8509.Springer,Cham, 2014. https://doi.org/10.1007/978-3-319-07998-1-72
[17] X. Gu, Z. Shen, J. Xue, Y. Fan and T. Ni “Brain Tumor MR image classification using convolutional dictionary learning with local constraint,”. Front. Neurosci. 15:679847,2021.doi: 10.3389/fnins.2021.679847.
[18] S. D. S. Al-Shaikhli, M. Y. Yang and B. Rosenhahn, “Brain tumor classification using sparse coding and dictionary learning,” IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 2774-2778, 2014. doi: 10.1109/ICIP.2014.7025561.
[19] G. Sandhya, A. Srinag, G.B. Pantangi, J.A., Kanaparthi, “Sparse coding for brain tumor segmentation based on the non-linear features,” JBBBE49,pp.63–73, 2021.https://doi.org/10.4028/www.scientific.net/jbbbe.49.63.
[20] G. S. Gopika, J. Shanthini, Muthu Subash Kavitha, and R. Sabitha “Brain tumor detection with biologically inspired watershed segmentation and classification based on feed-forward neural network,” (FNN). J. Medical Imaging Health Informatics 11(12): 3181-3190, 2021.
[21] S. Al-Shaikhli, S. Dawood, M.Ying and R., Bodo. “Brain tumor classification and segmentation using sparse coding and dictionary learning,” Biomedical Engineering / Biomedizinische Technik, vol. 61, no. 4, 2016, pp. 413-429. https://doi.org/10.1515/bmt-2015-0071.
[22] S. Mavaddati, “Classification of brain tumor using model learning based on statistical and texture features,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 19, no. 2, pp.177-188, 2022.
[23] M. Ghasemi, M. Kelarestaghi, F. Eshghi, and A. Sharifi, “T2-FDL: A robust sparse representation method using adaptive type-2 fuzzy dictionary learning for medical image classification,” Expert Systems with App., vol. 158, 2020.
[24] A. Bougacha, I. Njeh, J. Boughariou, O. Kammoun, B. Mahfoudh, M. Dammak, C. Mhiri, and B. A. Hamida “Rank-Two NMF clustering for glioblastoma characterization,”. J Health Eng. 2018 Oct 23; 2018:1048164. doi: 10.1155/2018/1048164. PMID: 30425818; PMCID: PMC6218733.
[25] S. Ortega-Martorell, P.J. Lisboa, and A. Vellido, ”Non-negative matrix factorization methods for the spectral decomposition of MRS data from human brain tumors,” BMC Bioinformatics, vol. 13, no. 38, 2012. https://doi.org/10.1186/1471-2105-13-38.
[26] S.Z. Kurdi, M.H. Ali, M.M. Jaber, T. Saba, A. Rehman, R. Damaševičius, “Brain tumor classification using meta-heuristic optimized convolutional neural networks,” J Pers Med., vol. 13, no. 2, 2023. doi: 10.3390/jpm13020181. PMID: 36836415; PMCID: PMC9965936.
[27] C.D. Sigg, T. Dikk, J. M., Buhmann, “Speech enhancement using generative dictionary learning,” IEEE Transactions on Audio, Speech and Language Processing, vol. 20, no. 6, pp.1698-1712, 2012.
[28] D. Barchiesi, M. D. Plumbley, “Learning incoherent dictionaries for sparse approximation using iterative projections and rotations,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 205565, 2013.
[29] Https://figshare.com/articles/brain_tumor_dataset/1512427.
[30] R. Achkar, M. Owayjan, B. Harajli, D. Khazaal, M. Dbouk and G. Magnifico, “Brain Tumor Classification Using Back Propagation Algorithm in MLP,” 2019 Fourth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Beirut, Lebanon, 2019, pp. 1-5, doi: 10.1109/ACTEA.2019.8851109.
[31] A. B. Malarvizhi, A. Mofika, M. Monapreetha, and A. M. Arunnagiri, “Brain tumour classification using machine learning algorithm,” J Phys Conf Ser, vol. 2318, no. 1, p. 012042, Aug. 2022, doi: 10.1088/1742-6596/2318/1/012042.
[32] S. Mavaddati, S. Mavaddati, “Rice classification and quality detection based on sparse non-negative matrix factorization,” Journal of Iranian Association of Electrical and Electronics Engineers, vol. 17, no. 1, pp. 11-21, 2020.