ARCHIVES
Original Article
Brain Tumor Detection and Classification Using Deep Learning
Mohammad Asif1
Dr. Khaja Mahabubullah2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Professor & HOD, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 85-90
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250605014
References
1. M. Saranya, “Accurate and real-time brain tumour detection and classification,” Scientific Reports, vol. 15, no. 7742, pp. 1–11, Jan. 2025.
2. M. H. O. Alnageeb, A. Ali, and S. Abdullah, “Real-time brain tumour diagnoses using MK-YOLOv8,” Computers in Biology and Medicine, vol. 178, pp. 107693, 2025.
3. Y. Wong, S. Lim, and K. Tan, “Brain tumor classification using MRI images and deep CNNs,” PLOS ONE, vol. 20, no. 3, pp. 1–14, Mar. 2025.
4. S. Iftikhar, A. Khan, and M. Zafar, “Explainable CNN for brain tumor detection and classification,” Brain Informatics, vol. 12, no. 5, pp. 1–12, 2025.
5. K. N. Rao, P. Gupta, and R. Singh, “An efficient brain tumor detection and classification,” Heliyon, vol. 10, no. 6, pp. e128046, 2024.
6. N. H. Lu, Y. Li, and J. Zhou, “Deep learning-driven brain tumor classification,” Scientific Reports, vol. 15, no. 3591, pp. 1–9, Feb. 2025.
7. R. R. Ali and M. H. Rahman, “Res-BRNet: Learning architecture for brain tumor classification,” arXiv preprint, arXiv: 2211.16571, pp. 1–14, 2025.
8. F. J. Díaz-Pernas, M. Martínez-Zarzuela, and D. González-Ortega, “Multiscale CNN for classification and segmentation of brain tumors,” arXiv preprint, arXiv: 2402.05975, pp. 1–12, 2024.
9. H. Dong, G. Yang, and F. Liu, “Automatic brain tumor detection and segmentation using U-Net,” arXiv preprint, arXiv: 1705.03820, pp. 1–10, 2017.
10. D. Filatov and G. N. A. H. Yar, “Classification of brain tumors via pre-trained CNNs,” arXiv preprint, arXiv: 2208.00768, pp. 1–8, 2022.
2. M. H. O. Alnageeb, A. Ali, and S. Abdullah, “Real-time brain tumour diagnoses using MK-YOLOv8,” Computers in Biology and Medicine, vol. 178, pp. 107693, 2025.
3. Y. Wong, S. Lim, and K. Tan, “Brain tumor classification using MRI images and deep CNNs,” PLOS ONE, vol. 20, no. 3, pp. 1–14, Mar. 2025.
4. S. Iftikhar, A. Khan, and M. Zafar, “Explainable CNN for brain tumor detection and classification,” Brain Informatics, vol. 12, no. 5, pp. 1–12, 2025.
5. K. N. Rao, P. Gupta, and R. Singh, “An efficient brain tumor detection and classification,” Heliyon, vol. 10, no. 6, pp. e128046, 2024.
6. N. H. Lu, Y. Li, and J. Zhou, “Deep learning-driven brain tumor classification,” Scientific Reports, vol. 15, no. 3591, pp. 1–9, Feb. 2025.
7. R. R. Ali and M. H. Rahman, “Res-BRNet: Learning architecture for brain tumor classification,” arXiv preprint, arXiv: 2211.16571, pp. 1–14, 2025.
8. F. J. Díaz-Pernas, M. Martínez-Zarzuela, and D. González-Ortega, “Multiscale CNN for classification and segmentation of brain tumors,” arXiv preprint, arXiv: 2402.05975, pp. 1–12, 2024.
9. H. Dong, G. Yang, and F. Liu, “Automatic brain tumor detection and segmentation using U-Net,” arXiv preprint, arXiv: 1705.03820, pp. 1–10, 2017.
10. D. Filatov and G. N. A. H. Yar, “Classification of brain tumors via pre-trained CNNs,” arXiv preprint, arXiv: 2208.00768, pp. 1–8, 2022.
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