Segmentation of Brain Tumors in MRI Images Utilizing the Modified U-Net Model
Bibliographic record
Abstract
Segmentation of brain tumors in MRI is essential for accurate diagnosis and treatment planning; however, manual annotation is labor intensive and subject to observer variation. This paper presents an automated segmentation method based on a U-Net architecture, which comprises of encoder-decoder skip connections, and can be optionally integrated with multi-head attention. We train the model on multi-view glioma MRI data by employing standard preprocessing techniques, which including normalization, skull stripping and slicing. Performance is measured by Pixel Accuracy Mean Accuracy, Mean IoU (Intersection over Union), Frequency Weighted IoU and Dice Score. Experimental verification on coronal, sagittal and transverse views demonstrates that the proposed U-Net reaches about 99 % pixel accuracy and has good generalization properties with similar convergence style. The comparative analysis has shown that, when compared to the competing methods (i.e., Thresholding, K-Means, Fuzzy C-Means) and the LinkNet model, U-Net can provide more consistent and clearer tumor boundaries. The results confirm U-Net as an efficient, real-time and improved method towards brain tumor segmentation towards eventual integration in clinical decision support systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".