International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Brain Tumour Classification: Methods, Technologies and a Comparative Review |
|---|---|
| ABSTRACT | One of the most dangerous neurological conditions is brain tumours, and patient survival and treatment planning are directly impacted by early and accurate diagnosis. The main non-invasive method for visualizing tumours is still magnetic resonance imaging (MRI), and computer-aided diagnosis (CAD) systems based on deep learning and machine learning have gradually automated the classification of meningioma, glioma, pituitary, and other tumour types. The main paradigms for brain tumour classification are compared in this review in an organized, mathematically grounded manner: (i) deep Convolutional Neural Networks (CNNs) and transfer-learning architectures such as VGG16/19, ResNet50, InceptionV3, Xception, DenseNet, MobileNet, and EfficientNet; (ii) classical machine learning pipelines that rely on handcrafted texture and statistical features such as the Gray-Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), and Principal Component Analysis (PCA). (g). ResViT, UNetFormer, TECNN); and (iv) ensemble and hybrid approaches that combine learned and manually created features. The underlying mathematical formulation for each family of methods is derived and explained, and performance figures from recent literature are tabulated on widely used benchmark datasets (Figshare CE-MRI, BraTS, Kaggle Brain Tumour MRI Dataset, Br35H, and SARTAJ). A comparison of accuracy-complexity trade-offs, an overview of unresolved issues, and future directions like multimodal fusion, explainable AI, and quantum machine learning round out the review. |
| AUTHOR | DR GURURAJ J.P Associate Professor, Government First Grade College, Davangere, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407071 |
| pdf/71_Brain Tumour Classification Methods, Technologies and a Comparative Review.pdf | |
| KEYWORDS | |
| References | [1]. Chen, Y., Giudici, P., Kolesnikov, V., & Recchia, P. (2026). SAFE Quantum Machine Learning with Variational Quantum Classifiers. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2605.16067. [2]. Veneri, P. (2026). Interpretable four-class brain tumor MRI classification using a fine-tuned ResNet50. PeerJ Comput. Sci., 12, e3643. [3]. Kurniawan, M. B., & Utami, E. (2025). Performance comparison of ReSNEt50, VGG16, and MobileNetV2 for brain tumor classification on MRI images. SISTEMASI, 14(2), 767. https://doi.org/10.32520/stmsi.v14i2.5054. [4]. Rasheed M, Iqbal S, Jaffar A, Akram S. Advanced deep learning-based brain tumor classification using a novel customized CNN and optimized residual network. PLoS One. 2025 Oct 10;20(10):e0334430. doi: 10.1371/journal.pone.0334430. [5]. Shahin, A. Fine-tuned ResNet34 for efficient brain tumor classification. Sci Rep 15, 36910 (2025). https://doi.org/10.1038/s41598-025-20872-3 [6]. Bouhafra, S. et al. (2025). Deep Learning Approaches for Brain Tumour Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review. Journal of Imaging Informatics in Medicine, 38(3), 1403-1433. [7]. Deep Learning for Brain Tumour Segmentation and Classification: A Systematic Review of Methods and Trends (2025). ScienceDirect. [8]. Kumar, P.R., Prakash, K., Teja, B.R.S. et al. A novel brain MRI classification framework integrating tuned single scale retinex and empirical wavelet entropy features. Sci Rep 15, 29765 (2025). https://doi.org/10.1038/s41598-025-13281-z. [9]. Khan, M. et al. (2022). Convolutional Neural Network Techniques for Brain Tumour Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. [10]. Sarah P, Krishnapriya S, Saladi S, Karuna Y and Bavirisetti DP (2024) A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation. Front. Physiol. 15:1342572. doi: 10.3389/fphys.2024.1342572. [11]. Reyes, D., & Sánchez, J. (2024). Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging. Heliyon, 10(3), e25468. https://doi.org/10.1016/j.heliyon.2024.e25468. [12]. Rustom, F., Moroze, E., Parva, P., Ogmen, H., & Yazdanbakhsh, A. (2024). Deep learning and transfer learning for brain tumour detection and classification. Biology Methods and Protocols, 9(1), bpae080. [13]. Khan, M. A., & Auvee, R. B. Z. (2024). Comparative Analysis of Resource-Efficient CNN Architectures for brain tumor Classification. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.15596. [14]. Dheepak, G., J, A. C., & Vaishali, D. (2024). Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Frontiers in Oncology, 13, 1248452. https://doi.org/10.3389/fonc.2023.1248452. [15]. Subba, A. B., & Sunaniya, A. K. (2024). Computationally optimized brain tumor classification using attention based GoogLeNet-style CNN. Expert Systems With Applications, 260, 125443. https://doi.org/10.1016/j.eswa.2024.125443. [16]. Abdusalomov, A. B. et al. (2024). Brain tumour detection based on deep learning approaches and magnetic resonance imaging. Cancers, 16(1), 1-19. [17]. Brain Tumour Classification using VGG16, ResNet50, and Inception V3 Transfer Learning Models (2023). [18]. Babu Vimala, B., Srinivasan, S., Mathivanan, S.K. et al. Detection and classification of brain tumor using hybrid deep learning models. Sci Rep 13, 23029 (2023). https://doi.org/10.1038/s41598-023-50505-6. [19]. Alemu, B. S., Feisso, S., Mohammed, E. A., & Salau, A. O. (2023). Magnetic resonance imaging-based brain tumor image classification performance enhancement. Scientific African, 22, e01963. https://doi.org/10.1016/j.sciaf.2023.e01963. [20]. Kumar, A., Pandey, S.K., varshney, N. et al. Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM. Sci Rep 13, 22735 (2023). https://doi.org/10.1038/s41598-023-50073-9. [21]. Sudeep, K. et al. (2022). Brain Tumour Classification using a Support Vector Machine. International Journal of Computer Applications, 184(28). [22]. Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022). Multi-modal brain tumour detection using deep neural network and multiclass SVM. Medicina, 58(8), 1090. [23]. Garg, G., & Garg, R. (2021). Brain Tumor Detection and Classification based on Hybrid Ensemble Classifier. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2101.00216. [24]. Khan, H. A. et al. (2020). Brain tumour classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering, 17(5), 6203-6216. [25]. Çinar, A., & Yildirim, M. (2020). Detection of tumours on brain MRI images using the hybrid convolutional neural network architecture. Medical Hypotheses, 139, 109684. [26]. A Novel Method for Classifying Liver and Brain Tumours Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks (2019). [27]. Sultan, H. H., Salem, N. M., & Al-Atabany, W. (2019). Multi-classification of brain tumour images using deep neural network. IEEE Access, 7, 69215-69225. |
