Color image processing and content-based image retrieval techniques for the analysis of dermatological lesions
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Bibliographic record
Abstract
This paper presents color image processing methods for the analysis of dermatological images in the context of a content-based image retrieval (CBIR) system. Tests were conducted on the classification of tissue components in skin lesions, in terms of necrotic tissue, fibrin, granulation, and mixed composition. The images were classified based on color components by an expert dermatologist following a black-yellow-red model. Indexing and retrieval of images were performed based on texture information obtained from the red, green, blue, hue, and saturation components of the color images. The performance of the CBIR system was measured in terms of precision and recall. Initial results demonstrate the potential of the proposed methods with the best precision result of 70% obtained for the characterization of mixed tissue composition.
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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 | 0.000 | 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) | 0.000 | 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 it