Bibliographic record
Abstract
INTRODUCTION Acne is a condition with severe physical and psychological implications on chronic sufferers, affecting approximately 80 percent of people between the ages of 11 and 30, at some point in their lives. The purpose of the research was to create a prototype that has the potential track changes in the condition for a person suffering from acne, in order to provide an accurate and standardized tool of assessment. Such a tool would be useful in both clinical and research settings where qualitative and quantifiable results are needed [1]. These are normally hard to obtain due to the low reproducibility and high subjectivity of the assessment which often leads to high inter and intra variability in graders. METHODS Images of (20) patients with healthy skin, acne, and other conditions similar in appearance to acne were used to test the algorithm, LS-KMC, that is explained in the schematic in Figure 1. The left side outlines the previous method based on Ramli et al.’s work [2], RMH-KMC, for comparison. RESULTS The results show that LS-KMC outclasses the previous system, not just by its ability to automatically characterize the condition in the region of interest (ROI), but also by achieving higher scores across all relevant metrics in 19 of the 20 cases. DISCUSSION AND CONCLUSIONS We proposed a new acne recognition approach to extend the previous k-clustering method proposed by Ramli et al. to introduce a higher level of computer aided support for providing acne scores. The new algorithm includes a number of steps to automate the identification and classification of acne features. Previously used metrics failed to take into account the nature of the condition and lead to unrealistically high success rates. A new metric, Characterization Sensitivity , was proposed for identifying the relative success of acne recognition algorithms. The metric uses a weighted area technique with assigned coefficients to the areas of the lesions based on the Michaelson grading scale [3]. The results showed that the proposed Lucut-Smith k-clustering algorithm performs much better than the previous 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".