- Home
- Archives
- 2025
- Vol. 6 Issue 6
- Article
Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification
Authors
1 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 59-66
- Article Type
- Original Article
- Check for updates
-
References
1. K. Kamnitsas, L. Chen, and D. Rueckert, “Multi-resolution convolutional neural networks for accurate brain lesion segmentation in MRI scans,” Medical Image Analysis, vol. 40,pp. 1–14, 2018.
2. F. Isensee, P. F. Jaeger, and K. H. Maier-Hein, “Automated biomedical image segmentation using adaptive deep learning frameworks,” Nature Methods, vol. 18, no. 2, pp. 200–208,2021.
3. F. Milletari, N. Navab, and S. Ahmadi, “Deep volumetric convolutional networks for medical image segmentation tasks,” IEEE Transactions on Medical Imaging, vol. 36, no. 12, pp. 1– 10, 2017.
4. M. Havaei, A. Davy, and D. Warde-Farley, “Brain tumor segmentation using convolutional neural networks with contextual information,” Medical Image Analysis, vol. 35, pp. 18–31, 2017.
5. L. Wang, J. Wang, and Y. Li, “Hierarchical convolutional models for automated brain tumor segmentation,” Computer Methods and Programs in Biomedicine, vol. 178, pp. 1–11, 2019.
6. S. Bakas, M. Reyes, and A. Jakab, “Benchmarking machine learning techniques for brain tumor segmentation and survival prediction,” arXiv preprint, arXiv:1811.02629, 2018.
7. A. Rehman, S. R. Khan, and Z. Mehmood, “Deep learning- based multiclass brain tumor classification using MRI images,” Applied Soft Computing, vol. 92, pp. 1–14, 2020.
8. J. Amin, M. Sharif, and M. Yasmin, “Deep learning approaches for automated brain tumor detection in medical images,” Future Generation Computer Systems, vol. 87, pp. 290–297, 2018.
9. Y. Zhou, H. Huang, and S. Li, “Attention-guided convolutional neural networks for medical image segmentation,” Pattern Recognition Letters, vol. 140, pp. 1–9, 2020.
10. R. Gupta and P. Malathi, “Recent advances in deep learning for brain tumor analysis using MRI,” Journal of Healthcare Engineering, vol. 2021, pp. 1–12, 202
2. F. Isensee, P. F. Jaeger, and K. H. Maier-Hein, “Automated biomedical image segmentation using adaptive deep learning frameworks,” Nature Methods, vol. 18, no. 2, pp. 200–208,2021.
3. F. Milletari, N. Navab, and S. Ahmadi, “Deep volumetric convolutional networks for medical image segmentation tasks,” IEEE Transactions on Medical Imaging, vol. 36, no. 12, pp. 1– 10, 2017.
4. M. Havaei, A. Davy, and D. Warde-Farley, “Brain tumor segmentation using convolutional neural networks with contextual information,” Medical Image Analysis, vol. 35, pp. 18–31, 2017.
5. L. Wang, J. Wang, and Y. Li, “Hierarchical convolutional models for automated brain tumor segmentation,” Computer Methods and Programs in Biomedicine, vol. 178, pp. 1–11, 2019.
6. S. Bakas, M. Reyes, and A. Jakab, “Benchmarking machine learning techniques for brain tumor segmentation and survival prediction,” arXiv preprint, arXiv:1811.02629, 2018.
7. A. Rehman, S. R. Khan, and Z. Mehmood, “Deep learning- based multiclass brain tumor classification using MRI images,” Applied Soft Computing, vol. 92, pp. 1–14, 2020.
8. J. Amin, M. Sharif, and M. Yasmin, “Deep learning approaches for automated brain tumor detection in medical images,” Future Generation Computer Systems, vol. 87, pp. 290–297, 2018.
9. Y. Zhou, H. Huang, and S. Li, “Attention-guided convolutional neural networks for medical image segmentation,” Pattern Recognition Letters, vol. 140, pp. 1–9, 2020.
10. R. Gupta and P. Malathi, “Recent advances in deep learning for brain tumor analysis using MRI,” Journal of Healthcare Engineering, vol. 2021, pp. 1–12, 202
Related Articles
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.