A Robust Approach to Segment Human Skin and Burnt Region from Chaos Background Using Classification Trees
Bibliographic record
Abstract
Accurate estimation of the total body surface area (TBSA) and its percentage is critical for efficient burn patient care. In this paper, we present a machine learning-based approach for segmenting burnt regions and healthy skin areas from the burn image dataset we collected, addressing challenges related to limited dataset size and chaotic hospital bkground. Our method utilizes classification tree model trained on features extracted from the HSV color space, normalized RG components, and Chroma values, then compare with ensemble trees (Random Forest and XGBoost). The results demonstrate robust performance in urtely segmenting burn regions and healthy skin, outperforming existing methodologies where reh 93.94% accuracy in healthy skin segmentation and 94.59% in burnt region segmentation. Additionally, we identify the need t augment the dataset with more diverse skin examples in future work to improve sensitivity in detecting healthy skin. Our roch provides valuable contribution to the accurate determination of TBS percentage, thereby streamlining the assessment and treatment process for burn patients.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".