Brain Tumor Detection Using 3D U-Net Segmentation Features and a Hybrid Machine Learning Classifier

References

[1] M. F. Ferreira et al., "Towards Accurate Brain Tumour Segmentation: A Deep Learning Approach Using U-Net Variants," Procedia Computer Science, 2026.

[2] D. LaBella et al., "The 2024 BraTS-MEN-RT dataset," Scientific Data, vol. 13, no. 306, 2026.

[3] W. Bukaita and V. Vadde, "Comparative Evaluation of CNN and ResNet18 for MRI-Based Brain Tumor Classification," Medical Research Archives, vol. 13, no. 12, 2025.

[4] J. Walsh et al., "Using U-Net for efficient brain tumor segmentation in MRI images," arXiv:2211.01885, 2022.

[5] J. Li et al., "Category guided attention network for brain tumor segmentation," arXiv:2203.15383, 2022.

[6] "Brain tumor imaging and genomics," Frontiers in Oncology, 2019.

[7] "Glioblastoma and neuro-oncology advances," Nature Reviews Neurology, 2017.

[8] "Support Vector Machine Algorithm," GeeksforGeeks.

[9] "Deep learning for brain tumor analysis," MDPI Computers, vol. 11, no. 2, 2022.

[10] "Random Forest Classifier using Scikit-Learn," GeeksforGeeks.

[11] "Gradient Boosting in Machine Learning," GeeksforGeeks.

[12] B. H. Menze et al., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," IEEE Trans. Medical Imaging, vol. 34, no. 10, pp. 1993–2024, 2015.

[13] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," MICCAI, 2015.

[14] Ö. Çiçek et al., "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation," MICCAI, 2016.

[15] P. Lambin et al., "Radiomics: Extracting more information from medical images," European Journal of Cancer, 2012.

[16] G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, 2017.

[17] T. G. Dietterich, "Ensemble Methods in Machine Learning," Multiple Classifier Systems, 2000.

@article{1717163,
      author = {Nindujerla Sandeep Rao, Kota Sai Vidith, Korabandi Ajay Babu, Dr. N. Sudheer Kumar},
      title = {Brain Tumor Detection Using 3D U-Net Segmentation Features and a Hybrid Machine Learning Classifier},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3822-3825},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717163.pdf},
      abstract = {Brain tumors killed roughly 250,000 people worldwide in 2020, with approximately 300,000 new diagnoses that same year. The most aggressive type, Glioblastoma Multiforme (GBM), carries a median survival of just 15 months and a five-year survival rate under 5%. A key clinical complication is the MGMT gene promoter: its methylation status determines how well a patient respond to chemotherapy, but establishing it currently requires an invasive biopsy. This paper proposes a non-invasive framework combining 3D U-Net MRI segmentation with a soft-vote hybrid classifier (KNN + GBC). On the RSNA-MICCAI dataset (585 samples), 3D U-Net segmentation yields 111 volumetric radiomic features versus 54 from a 2D U-Net. The hybrid model achieves 99.4% classification accuracy on the richer 3D features, well above deep learning baselines (39–49%) tested on the same data, confirming that feature quality and ensemble diversity outperform model complexity on small clinical datasets.},
      keywords = {brain tumor detection, 3D U-Net segmentation, radiomic features, hybrid ensemble learning, gradient boosting, MGMT prediction},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1717163}
  }