Journal of Modeling and Simulation in Electrical and Electronics Engineering

Journal of Modeling and Simulation in Electrical and Electronics Engineering

Machine Learning Techniques in Brain Tumor Diagnosis using MRI

Document Type : Review Article

Authors
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.
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
Subjects

[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 neuropathologica, 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, pp. 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, pp. 111, 2019.
[4]     A. M. Sarhan, “Brain tumor classification in magnetic resonance images using deep learning and wavelet transform,” Journal of Biomedical Science and Engineering, 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,” The Visual Computer, 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,” Journal of Digital 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,” Biomedical Signal Processing and 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,” Magnetic resonance 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,” Journal of Neurology Research, vol. 10, no. 5, pp. 173-176, 2020.
[14]   J. L. Ashtekar. "Intracranial hemorrhage evaluation with MRI," 13, 2023; http://wwwemedicin.medscape.com.
[15]   St. Vincent’s University Hospital Radiology Department. "Multiple Sclerosis Enhancing Plaque," 13, 2023; 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),” Microscopy research and technique, 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,” The Visual Computer, 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,” International Journal of Computer Science & Network Security, 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 Chemie Int Ed, vol. 6, no. 11, pp. 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, pp. 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," Supervised and Unsupervised Learning for Data Science, pp. 3-21: Springer, 2020.
[26]   S. Ali, and J. Agrawal, “Automated segmentation of brain tumour images using deep learning-based model VGG19 and ResNet 101,” Multimedia Tools and Applications, vol. 83, no. 11, pp. 33351-33370, 2024.
[27]   A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, 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 Computing and Applications, 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,” Scientific Reports, vol. 14, no. 1, pp. 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,” International Journal of Imaging Systems and Technology, vol. 34, no. 1, pp. 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 Systems with Applications, vol. 238, pp. 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,” Biomedical Signal Processing and Control, vol. 109, pp. 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,” Journal of Cloud Computing, vol. 12, no. 1, pp. 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,” International Journal of Information Technology, 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 Medical Imaging, vol. 24, no. 1, pp. 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,” Computational Biology and Chemistry, vol. 117, pp. 108381, 2025.
[43]   Y. Cheng, Z. Liu, and S. Tamura, “Deep Hierarchy-Aware Segmentation: A Novel Framework for MRIs Brain Tumor Segmentation,” IEEE Transactions on Medical 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 Transactions on Medical 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,” Medical Physics, vol. 52, no. 8, pp. 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,” Medical Image Analysis, vol. 101, pp. 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 Computing and Applications, 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,” Computers in Biology and Medicine, vol. 168, pp. 107723, 2024.
[49]   P. Kanchanamala, V. Kuppusamy, and G. Ganesan, “QDCNN-DMN: A hybrid deep learning approach for brain tumor classification using MRI images,” Biomedical Signal Processing and Control, vol. 101, pp. 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 in Engineering, vol. 25, pp. 104327, 2025.

Articles in Press, Corrected Proof
Available Online from 07 September 2026

  • Receive Date 14 January 2026
  • Revise Date 19 June 2026
  • Accept Date 17 August 2026