Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01033
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202532801033
Published online 18 June 2025
  1. A.B. Abdusalomov, M. Mukhiddinov, and T.K. Whangbo, "Brain tumour detection based on deep learning approaches and magnetic resonance imaging," PMC, 2023. https://doi.org/10.3390/jimaging10090235 [Google Scholar]
  2. A. Abdusalomov et al., "Enhancing automated brain tumour detection accuracy using artificial intelligence approaches for healthcare environments," Bioengineering, vol. 11, no. 6, p. 627, 2024, https://doi.org/10.3390/bioengineering11060627 [CrossRef] [PubMed] [Google Scholar]
  3. A. Aleid et al., "Artificial intelligence approach for early detection of brain tumours using MRI images," Applied Sciences, vol. 13, no. 6, p. 3808, 2023, https://doi.org/10.3390/app13063808 [CrossRef] [Google Scholar]
  4. M. Arabahmadi et al., "Deep learning for smart healthcare-A survey on brain tumour detection from medical imaging," PMC, 2023. https://doi.org/10.3390/s22051960 [Google Scholar]
  5. T. Hossain, F.S. Shishir, M. Ashraf, M.A. Al Nasim, and F.M. Shah, "Brain tumour detection using convolutional neural network," in Proc. 1st Int. Conf. Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1–6, https://doi.org/10.1109/ICASERT.2019.8934561 [Google Scholar]
  6. Kaggle. (2023). Brain tumour classification MRI dataset. link: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumour-classification-mri [Google Scholar]
  7. M.Z. Khaliki, "Brain tumour detection from images and comparison with transfer learning methods and 3-layer CNN," Scientific Reports, vol. 14, 2024, https://doi.org/10.1038/s41598-024-52823-9 [CrossRef] [Google Scholar]
  8. K. Kumar, K. Jyoti, and K. Kumar, "Machine learning for brain tumour classification: Evaluating feature extraction and algorithm efficiency," Springer, 2024, http://dx.doi.org/10.1007/s44163-024-00214-4 [Google Scholar]
  9. S. Saeedi and S. Rezayi, "MRI-based brain tumour detection using convolutional deep learning methods and chosen machine learning techniques," BMC Medical Informatics and Decision Making, vol. 23, 2023, https://doi.org/10.1186/s12911-023-02114-6 [CrossRef] [Google Scholar]
  10. A.S.M. Shafi, "Classification of brain tumours and auto-immune disease using ensemble learning," Heliyon, vol. 7, no. 5, 2021, https://doi.org/10.1016/j.heliyon.2021.e06988 [Google Scholar]
  11. A. Sinha, M. Suresh, N. Mohan, and A.G. Singerji, "Brain tumour detection using deep learning," in Proc. Seventh Int. Conf. Bio Signals, Images, and Instrumentation (ICBSII), 2021, https://doi.org/10.1109/ICBSII51839.2021.9445185 [Google Scholar]
  12. T.A. Soomro, "Image segmentation for MR brain tumour detection using machine learning: A review," PubMed, 2022. https://doi.org/10.1109/rbme.2022.3185292 [Google Scholar]
  13. A. Tiwari, S. Srivastava, and M. Pant, "Brain tumour segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019," Pattern Recognition Letters, vol. 129, pp. 506–512, 2020, https://doi.org/10.1016/i.patrec.2019.11.020 [Google Scholar]
  14. R. Wang, "An artificial intelligence-based approach to brain tumour prediction," in Proc. ACM, 2024. https://doi.org/10.1145/3652628.3652797 [Google Scholar]
  15. D.S. Wankhede and R. Selvarani, "Dynamic architecture-based deep learning approach for glioblastoma brain tumour survival prediction," Neuroscience Informatics, vol. 2, p. 100049, 2022, https://doi.org/10.1016/i.neuri.2022.100062 [CrossRef] [Google Scholar]
  16. D.S. Wankhede, C.J. Shelke, and A. George, "An enhanced algorithm for predicting IDH1 mutations and 1p19q mitigation in glioma tumour," in AIP Conf. Proc., vol. 3217, no. 1, p. 020025, 2024, https://doi.org/10.1063/5.0237441 [CrossRef] [Google Scholar]
  17. D.S. Wankhede, C.J. Shrivastava, and V.K. Achary, "Brain tumour detection and classification using adjusted Inceptionv3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN model," EAI Endorsed Transactions on Pervasive Health and Technology, vol. 10, 2024, https://doi.org/10.4108/eetpht.10.6377 [CrossRef] [Google Scholar]
  18. M.M. Zahoor et al., "A new deep hybrid boosted and ensemble learning-based brain tumour analysis using MRI," PMC, 2023. https://doi.org/10.3390/s22072726 [Google Scholar]
  19. Zhou, X., Li, Y., & Chen, X. (2020). Ensemble of Deep Neural Networks for Small Dataset Medical Image Classification. IEEE Transactions on Biomedical Engineering, 67(4), 1057–1066. https://doi.org/10.1109/TBME.2019.2954054 [Google Scholar]

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