ARCHIVES
Original Article
Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification
Dr. B Harichandana1
Karanth J2
Dattatri3
Bhargav C4
1 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 59-66
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250606009
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
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