2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 325-339, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed Nazneen Fathima 1 * , Prabhjot Singh1 2 , Simrandeep Singh 3
Doi: https://doi.org/10.54216/JISIoT.170221
The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis and/or treatment planning. The existing techniques suffer the effects of scale variation, redundant features, and the high dimensionality that causes ambiguous findings. We suggest the model named MFWX, which unites Multi-Scale CNN, Multi-Frequency Channel Attention (MFCA), Weighted Particle Swarm Optimization (WPSO) to identify features and XGBoost methods to classify them. The Multi-Scale CNN will capture the structure of the tumor at multiple resolutions, MFCA adjusts the features by zeroing in on significant frequency zones and WPSO eliminates redundancy to heavy-hit the strong forecasts of XGBoost. However, MFWX attained 94.2 accuracy and 92.5 Dice on the BraTS-2020 dataset surpassing ResNet50, EfficientNet-B7, and U-Net. It achieved an accuracy of 96.7%, and Dice of 95.1% on BraTS-2018, and performed well on classes of tumors. Ablation experiments proved the necessity of every part. In general, MFWX presents an efficient, clinically meaningful, scalable solution that outsmarts the current segmentation techniques.
Brain , Transformation , Deep learning , Segmentation , Classification
[1] L. L. Scientific, “Optimized deep learning framework for brain tumor detection and classification using Hybrid VGG-16,” Journal of Theoretical and Applied Information Technology, vol. 102, no. 16, pp. 230–245, 2024.
[2] Y. Amri, A. B. Slama, Z. Mbarki, and R. Selmi, “Automatic glioma segmentation based on Efficient U-Net model using MRI images,” Intelligence-Based Medicine, vol. 6, 2025.
[3] M. A. Aish, J. Ahmad, F. Nasim, and M. J. Iqbal, “Brain tumor segmentation and classification using ResNet50 and U-Net with TCGA-LGG and TCIA MRI scans,” Journal of Computing and Biomedical Informatics, vol. 4, pp. 115–132, 2024.
[4] P. Chowdhury and G. Srivastava, “Enhanced classification and segmentation of brain tumors in MRI images using Custom CNN and U-Net models with XAI,” in Proc. Int. Conf. Pattern Recognition and Machine Intelligence, Springer, 2024.
[5] N. Ahmad and Y. T. Chen, “Enhanced deep learning model performance in 3D multimodal brain tumor segmentation with Gabor filter,” in Proc. 10th Int. Conf. Computational Intelligence and Applications (ICCIA), IEEE, 2024.
[6] G. R. Srivastava, P. Gera, R. Rani, and G. Jaiswal, “A novel method for glioma segmentation and classification on pre-operative MRI scans using 3D U-Nets and transfer learning,” Multimedia Tools and Applications, vol. 84, no. 7, pp. 4301–4325, 2024.
[7] V. Satushe, V. Vyas, and S. Metkar, “Advanced CNN architecture for brain tumor segmentation and classification using BraTS-2018 dataset,” Current Medical Imaging, vol. 13, pp. 345–360, 2025.
[8] M. Zhang, J. Wang, X. Cao, X. Xu, J. Zhou, and H. Chen, “An integrated global and local thresholding method for segmenting blood vessels in angiography,” Heliyon, vol. 10, no. 5, pp. 123–140, 2024.
[9] N. Ahmad and Y. T. Chen, “3D brain tumor segmentation in multimodal MRI images,” in Proc. Int. Conf. Computer Vision and Applications (ICVA), IEEE, 2024.
[10] H. Huang et al., “A deep multi-task learning framework for brain tumor segmentation,” Frontiers in Oncology, Jun. 2021.
[11] S. Csaholczi, D. Iclanzan, L. Kovács, and L. Szilágyi, “Brain tumor segmentation from multi-spectral MR image data using Random Forest classifier,” in Lecture Notes in Computer Science, 2020.
[12] P. Ahmad, “MS UNet: Multi-scale 3D UNet for brain tumor segmentation,” in Lecture Notes in Computer Science, Jan. 2022.
[13] R. Yousef et al., “U-Net-based models towards optimal MR brain image segmentation,” Diagnostics, May 2023.
[14] M. Pathak, “Mathematical modelling for brain tumor segmentation and classification using machine learning,” Panamerican Mathematical Journal, Oct. 2024.
[15] V. K. Vaidyanathan, S. R. Pattanaik, and V. S. Kumar, “A MobileUnet++-based abnormality segmentation and multi-scale network approach for brain tumor classification,” in Proc. Int. Conf. Advances in Computing and Information Systems (IACIS), Aug. 2024.
[16] R. Breesha, T. R. D. Kumar, V. Ravi, V. R. K., and G. R. P., “Segmentation and classification of brain tumor using CNN algorithm,” in Proc. Int. Conf. Computing, Power and Communication Technologies (ICCPCT), Aug. 2024.
[17] G. Li, Y. Zhang, and Y. Luo, “Multi-task cascaded attention network for brain tumor segmentation and classification,” in Proc. IEEE ICASSP, Apr. 2024.
[18] J. N. Benedict, S. Shanmugapriya, S. P. S., and P. Kumar, “Accurate segmentation and classification of brain tumor using deep learning approaches,” in Proc. Int. Conf. Advanced Information Technologies (ICAIT), Jul. 2024.
[19] S. Kakarwal, “A novel approach for detection, segmentation, and classification of brain tumors in MRI images using neural network and special C Means fuzzy clustering techniques,” Advances in Nonlinear Variational Inequalities, Aug. 2024.
[20] Y. Dash et al., “Enhancing brain tumor classification and segmentation using ResNet50,” in Proc. IEEE ICCCNT, Jun. 2024.
[21] C. P. ThamilSelvi, V. K. S., R. R. Asaad, P. Palanisamy, and L. K. Rajappan, “An integrative framework for brain tumor segmentation and classification using Neuraclassnet,” Intelligent Data Analysis, Jun. 2024.
[22] M. Disha, A. Patro, K. Das, S. Roy, and B. J. R. Sahu, “Brain tumor segmentation and classification from MR images with feature extraction,” in Advances in Medical Image Processing, Jun. 2024.
