Hue-Preserved Quantile-Based Global Contrast Enhancement of Breast Thermograms for Abnormality Detection
Bibliographic record
Abstract
With breast cancer cases rapidly increasing worldwide, thermography serves as a potential imaging modality for early abnormality detection. Breast thermograms are low-resolution pseudocolored images that need contrast enhancement for better differentiability between normal and abnormal breasts. Without hue-preservation, conventional contrast enhancement techniques distort the color spaces and, hence, cannot be applied for contrast enhancement of color images, such as breast thermograms. This letter proposes a novel hue-preserving quantile-based global contrast enhancement (QGCE) of breast thermograms for abnormality detection. A comparative analysis of different types of contrast enhancement techniques with and without hue-preservation is shown. An exhaustive comparison of abnormality detection performance in the presence of different types of noises is also presented. The proposed technique achieves noise resilient detection accuracy of 91%. All evaluations are done using 85 breast thermograms from publicly available datasets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".