Automatic Detection of Necrotizing Fasciitis: A Dataset and Early Results
Bibliographic record
Abstract
Necrotizing Fasciitis (NF), or Necrotizing Soft-Tissue Infection (NSTI), is a rare infection that poses a significant threat to health. In the absence of a proper diagnosis, the infection can spread rapidly causing extensive tissue necrosis and death - mortality rate of 20% - 35%. Due to inadequate resources, little progress has been made for the automatic detection of NF. We have prepared a novel dataset containing images of affected human organs by NF using an internet image search. The dataset contains 693 images in total, containing raw, augmented, and non-NF images. A system has been developed for performing automated detection of NF with an Artificial Neural Network. We have evaluated the YOLOv3 object recognition model for five arrangements of our dataset and compared the performance for these different data arrangements after running each five times. The datasets were split into 80% train data and 20% test data, and for performance measures, we have taken into account the evaluation metrics: Intersection over Union (IoU) and Average Precision (AP). We obtained the highest average AP score of 57.97% for the dataset with raw data and augmentation and the highest average IoU score of 61.94% for dataset with raw data, augmentation, and negative images. The initial finding of this work can be further improved and become a substantial contribution to clinical arrangements for the diagnosis and management of NF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".