Enhanced U-Net Architecture for Brain Tumor Localization and Segmentation in T1-Weighted MRI
Bibliographic record
Abstract
Detection and segmentation of Magnetic Resonance Imaging (MRI) scans is a critical task in medical imaging, where achieving high segmentation precision and reliability remains challenging due to variations in tumor shape, size, intensity, and boundary definition across different MRI modalities. Recently, deep learning techniques have significantly improved the efficiency oflocalization and segmentation of various medical fields, including brain tumoursanalysis. This brief presents a novel two-stage approach for brain tumour segmentation in T1-weighted contrast-enhanced MRI (CE-MRI) scans, leveraging both YOLO (you only look once) and Modified U-Net. In the first stage, YOLO is employed to quickly and accurately localize regions of interest (ROIs) where brain tumours are present. To accelerate this step, YOLOv3 are incorporated, improving the computation speed and efficiency of the model. In the second stage, a modified U-NET model, enhanced with spatial and channel attention modules, is utilized to perform precise segmentation of the tumours within the identified ROIs. The performance of the proposed framework is evaluated using metrics including precision, recall, Jaccard index, and Dice similarity coefficient (DSC). Our approach demonstrates promising results compared to previous methods using the same database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".