| _version_ | 1866902326581657600 |
|---|---|
| author | Ramanujam, Raghav Sarangan |
| author_facet | Ramanujam, Raghav Sarangan |
| contents | <p>This paper presents a reproducible 2D U-Net baseline for binary brain tumor segmentation trained on a publicly available MRI dataset (Nikhil Roxtomar, Kaggle, 3,064 image–mask pairs). The work makes three explicit contributions beyond a standard implementation: (1) principled loss function selection using a manually implemented BCE + Dice combined loss, motivated by published hybrid loss comparisons; (2) a metric selection rationale that excludes pixel accuracy from model selection due to class imbalance inflation, using Dice coefficient as the primary criterion; and (3) a structured error taxonomy that categorises failure cases by attributed cause rather than visual appearance alone.</p> <p>The model achieves a mean Dice of 0.5579 and IoU of 0.4254 on non-empty test images (mean Dice 0.4708 across all 64 test images). Training was conducted on consumer-grade hardware (NVIDIA GTX 1650 Ti, 4 GB VRAM) and completed in 58 minutes. The best checkpoint was selected at Epoch 11 based on validation Dice. Per-image Dice and IoU scores for all test images are available in the repository.</p> <p>All code, model checkpoints, training scripts, and per-image evaluation results are publicly available at: <a href="https://github.com/RaghavSarangan2003/brain_tumor_segmentation_using_UNET">https://github.com/RaghavSarangan2003/brain_tumor_segmentation_using_UNET</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18989161 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reproducible 2D U-Net Baseline for Brain Tumor Segmentation with Structured Error Analysis Ramanujam, Raghav Sarangan <p>This paper presents a reproducible 2D U-Net baseline for binary brain tumor segmentation trained on a publicly available MRI dataset (Nikhil Roxtomar, Kaggle, 3,064 image–mask pairs). The work makes three explicit contributions beyond a standard implementation: (1) principled loss function selection using a manually implemented BCE + Dice combined loss, motivated by published hybrid loss comparisons; (2) a metric selection rationale that excludes pixel accuracy from model selection due to class imbalance inflation, using Dice coefficient as the primary criterion; and (3) a structured error taxonomy that categorises failure cases by attributed cause rather than visual appearance alone.</p> <p>The model achieves a mean Dice of 0.5579 and IoU of 0.4254 on non-empty test images (mean Dice 0.4708 across all 64 test images). Training was conducted on consumer-grade hardware (NVIDIA GTX 1650 Ti, 4 GB VRAM) and completed in 58 minutes. The best checkpoint was selected at Epoch 11 based on validation Dice. Per-image Dice and IoU scores for all test images are available in the repository.</p> <p>All code, model checkpoints, training scripts, and per-image evaluation results are publicly available at: <a href="https://github.com/RaghavSarangan2003/brain_tumor_segmentation_using_UNET">https://github.com/RaghavSarangan2003/brain_tumor_segmentation_using_UNET</a></p> |
| title | Reproducible 2D U-Net Baseline for Brain Tumor Segmentation with Structured Error Analysis |
| url | https://doi.org/10.5281/zenodo.18989161 |