Machine Learning Techniques in Brain Tumor Diagnosis using MRI

Khoshbakhtloo, Pedram; Rezai, Abdalhossein; Amiri, Nasrin

doi:10.22075/mseee.2026.40327.1247

Machine Learning Techniques in Brain Tumor Diagnosis using MRI

Document Type : Research Article

Authors

Pedram Khoshbakhtloo ¹

Abdalhossein Rezai ²

Nasrin Amiri ³

¹ Department of Medical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

² Department of Biomedical Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran.

³ Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

10.22075/mseee.2026.40327.1247

Abstract

Brain tumors require early and precise diagnosis to prevent severe clinical consequences. The complexity of tumor characteristics necessitates expert analysis through MRI, a process that is traditionally labor-intensive. To address this, automated systems leveraging Machine Learning (ML) architectures have emerged. This study evaluates the potential of high-precision computational frameworks for the automated detection and classification of brain tumors in MRI scans. A review of 18 recent studies highlights the effectiveness of ML methods, particularly Convolutional Neural Networks (CNNs), achieving up to 99.85% accuracy. Our research confirms that various ML methods can reliably identify pathological tissues, allowing for the assessment of tumor histology (type) and malignancy grade. Furthermore, the use of multimodal data, such as incorporating PET scans alongside MRI, is recommended to enhance diagnostic outcomes. While CNN-based models show promising results, they require extensive training data; therefore, transfer learning and hybrid models are encouraged to mitigate data scarcity and improve detection accuracy.

Keywords

Brain tumors

Brain abnormalities

Image processing

Machine learning

MRI

Subjects

Biomedical Engineering

[1] D. N. Louis, A. Perry, G. Reifenberger, A. von Deimling, D. Figarella-Branger, W. K. Cavenee, H. Ohgaki, O. D. Wiestler, P. Kleihues, and D. W. Ellison, “The 2016 World Health Organization classification of tumors of the central nervous system: A summary,” Acta Neuropathol., vol. 131, no. 6, pp. 803–820, 2016.

[2] J. Kang, Z. Ullah, and J. Gwak, “MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers,” Sensors, vol. 21, no. 6, Art. no. 2222, 2021.

[3] G. S. Tandel, M. Biswas, O. G. Kakde, A. Tiwari, H. S. Suri, M. Turk, J. R. Laird, C. K. Asare, A. A. Ankrah, and N. Khanna, “A review on a deep learning perspective in brain cancer classification,” Cancers, vol. 11, no. 1, Art. no. 111, 2019.

[4] A. M. Sarhan, “Brain tumor classification in magnetic resonance images using deep learning and wavelet transform,” J. Biomed. Sci. Eng., vol. 13, no. 6, pp. 102–112, 2020.

[5] Y. Guan, M. Aamir, Z. Rahman, A. Ali, W. A. Abro, Z. A. Dayo, M. S. Bhutta, Z. Hu, Y. Guan, and M. Aamir, “A framework for efficient brain tumor classification using MRI images,” Math. Biosci. Eng., vol. 18, no. 5, pp. 5790–5815, 2021.

[6] P. Afshar, K. N. Plataniotis, and A. Mohammadi, “Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries,” pp. 1368–1372.

[7] F. J. Díaz-Pernas, M. Martínez-Zarzuela, M. Antón-Rodríguez, and D. González-Ortega, “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,” p. 153.

[8] A. Dixit and A. Nanda, “An improved whale optimization algorithm-based radial neural network for multi-grade brain tumor classification,” Visual Comput., vol. 38, no. 11, pp. 3525–3540, 2022.

[9] S. Deepa, J. Janet, S. Sumathi, and J. Ananth, “Hybrid optimization algorithm enabled deep learning approach brain tumor segmentation and classification using MRI,” J. Digit. Imaging, vol. 36, no. 3, pp. 847–868, 2023.

[10] G. Mohan and M. M. Subashini, “MRI based medical image analysis: Survey on brain tumor grade classification,” Biomed. Signal Process. Control, vol. 39, pp. 139–161, 2018.

[11] A. Wadhwa, A. Bhardwaj, and V. S. Verma, “A review on brain tumor segmentation of MRI images,” Magn. Reson. Imaging, vol. 61, pp. 247–259, 2019.

[12] V. Kumar, A. K. Abbas, and J. C. Aster, Robbins and Cotran Pathologic Basis of Disease, 9th ed. Elsevier, 2015.

[13] A. Taree, V. Eslami, and S. Emamzadehfard, “Approach to brain magnetic resonance imaging for non-radiologists,” J. Neurol. Res., vol. 10, no. 5, pp. 173–176, 2020.

[14] J. L. Ashtekar, “Intracranial hemorrhage evaluation with MRI,” 2023. [Online]. Available: http://wwwemedicin.medscape.com

[15] St. Vincent’s University Hospital Radiology Department, “Multiple sclerosis enhancing plaque,” 2023. [Online]. Available: http://www.svuhradiology.ie/case-study/multiple-sclerosisenhancing-plaque

[16] S. Abbasi and F. Tajeripour, “Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient,” Neurocomputing, vol. 219, pp. 526–535, 2017.

[17] S. Iqbal, M. U. Ghani, T. Saba, and A. Rehman, “Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN),” Microsc. Res. Tech., vol. 81, no. 4, pp. 419–427, 2018.

[18] R. Singh, A. Goel, and D. K. Raghuvanshi, “Computer-aided diagnostic network for brain tumor classification employing modulated Gabor filter banks,” Visual Comput., vol. 37, no. 8, pp. 2157–2171, 2021.

[19] S. M. Kulkarni and G. Sundari, “Transfer learning using convolutional neural network architectures for glioma classification from MRI images,” Int. J. Comput. Sci. Netw. Secur., vol. 21, no. 2, pp. 198–204, 2021.

[20] Ç. Özkaya and Ş. Sağiroğlu, “Glioma grade classification using CNNs and segmentation with an adaptive approach using histogram features in brain MRIs,” IEEE Access, vol. 11, pp. 52275–52287, 2023.

[21] A. Tricco, “PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation,” Angew. Chem. Int. Ed., vol. 6, no. 11, p. 951, 1967.

[22] M. J. Page, J. E. McKenzie, and P. M. Bossuyt, “PRISMA 2020 explanation and elaboration: Updated guidance and exemplars,” BMJ, vol. 372, Art. no. n160, 2021.

[23] U. Greensboro, “Machine learning models and algorithms for big data classification,” 2016.

[24] M. W. Berry, A. Mohamed, and B. W. Yap, Supervised and Unsupervised Learning for Data Science. Springer, 2020.

[25] M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A systematic review on supervised and unsupervised machine learning algorithms for data science,” in Supervised and Unsupervised Learning for Data Science. Springer, 2020, pp. 3–21.

[26] S. Ali and J. Agrawal, “Automated segmentation of brain tumour images using deep learning-based model VGG19 and ResNet 101,” Multimedia Tools Appl., vol. 83, no. 11, pp. 33351–33370, 2024.

[27] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Adv. Neural Inf. Process. Syst., vol. 25, 2012.

[28] M. Amin, D. Shehwar, A. Ullah, T. Guarda, T. A. Tanveer, and S. Anwar, “A deep learning system for health care IoT and smartphone malware detection,” Neural Comput. Appl., vol. 34, no. 14, pp. 11283–11294, 2022.

[29] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

[30] M. Z. Khaliki and M. S. Başarslan, “Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN,” Sci. Rep., vol. 14, no. 1, Art. no. 2664, 2024.

[31] H. M. T. Khushi, T. Masood, A. Jaffar, S. Akram, and S. M. Bhatti, “Performance analysis of state-of-the-art CNN architectures for brain tumour detection,” Int. J. Imaging Syst. Technol., vol. 34, no. 1, Art. no. e22949, 2024.

[32] A. Akter, N. Nosheen, S. Ahmed, M. Hossain, M. A. Yousuf, M. A. A. Almoyad, K. F. Hasan, and M. A. Moni, “Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor,” Expert Syst. Appl., vol. 238, Art. no. 122347, 2024.

[33] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” pp. 1251–1258.

[34] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” pp. 770–778.

[35] A. Hekmat, Z. Zhang, S. U. R. Khan, and O. Bilal, “Brain tumor diagnosis redefined: Leveraging image fusion for MRI enhancement classification,” Biomed. Signal Process. Control, vol. 109, Art. no. 108040, 2025.

[36] F. Bal and F. Kayaalp, “A novel deep learning-based hybrid method for the determination of productivity of agricultural products: Apple case study,” IEEE Access, vol. 11, pp. 7808–7821, 2023.

[37] M. S. Başarslan and F. Kayaalp, “MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-based deep learning model for social media sentiment analysis,” J. Cloud Comput., vol. 12, no. 1, Art. no. 5, 2023.

[38] A. Pinto, S. Pereira, H. Correia, J. Oliveira, D. M. Rasteiro, and C. A. Silva, “Brain tumour segmentation based on extremely randomized forest with high-level features,” pp. 3037–3040.

[39] N. Abdullah, U. K. Ngah, and S. A. Aziz, “Image classification of brain MRI using support vector machine,” pp. 242–247.

[40] B. C. Mohanty, P. Subudhi, R. Dash, and B. Mohanty, “Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification,” Int. J. Inf. Technol., vol. 16, no. 3, pp. 1617–1626, 2024.

[41] S. Srinivasan, D. Francis, S. K. Mathivanan, H. Rajadurai, B. D. Shivahare, and M. A. Shah, “A hybrid deep CNN model for brain tumor image multi-classification,” BMC Med. Imaging, vol. 24, no. 1, Art. no. 21, 2024.

[42] P. V. Kusuma and S. C. M. Reddy, “Brain tumor segmentation and classification using MRI: Modified SegNet model and hybrid deep learning architecture with improved texture features,” Comput. Biol. Chem., vol. 117, Art. no. 108381, 2025.

[43] Y. Cheng, Z. Liu, and S. Tamura, “Deep hierarchy-aware segmentation: A novel framework for MRIs brain tumor segmentation,” IEEE Trans. Med. Imaging, 2025.

[44] Y. Lin, X. Fang, D. Zhang, K.-T. Cheng, and H. Chen, “Boosting convolution with efficient MLP-permutation for volumetric medical image segmentation,” IEEE Trans. Med. Imaging, vol. 44, no. 5, pp. 2341–2352, 2025.

[45] J. S. Yang, X. Yang, and X. Xiao, “Multi-class brain tumor diagnosis using MRI: A dynamic reinforcement ensemble learning approach,” Med. Phys., vol. 52, no. 8, Art. no. e18003, 2025.

[46] Q. Chen, L. Wang, Z. Deng, R. Wang, L. Wang, C. Jian, and Y.-M. Zhu, “Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping,” Med. Image Anal., vol. 101, Art. no. 103435, 2025.

[47] M. I. Sharif, J. P. Li, M. A. Khan, S. Kadry, and U. Tariq, “M3BTCNet: Multi model brain tumor classification using metaheuristic deep neural network features optimization,” Neural Comput. Appl., vol. 36, no. 1, pp. 95–110, 2024.

[48] R. Ranjbarzadeh, P. Zarbakhsh, A. Caputo, E. B. Tirkolaee, and M. Bendechache, “Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm,” Comput. Biol. Med., vol. 168, Art. no. 107723, 2024.

[49] P. Kanchanamala, V. Kuppusamy, and G. Ganesan, “QDCNN-DMN: A hybrid deep learning approach for brain tumor classification using MRI images,” Biomed. Signal Process. Control, vol. 101, Art. no. 107199, 2025.

[50] A. Batool and Y.-C. Byun, “A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images,” Results Eng., vol. 25, Art. no. 104327, 2025.