A Unified Neural Network Framework for Consistent and Efficient Real-time Object Detection of Early Longitudinal Melanonychia
Bibliographic record
Abstract
This study presents a unified neural network framework for the early detection of longitudinal melanonychia, leveraging deep learning and computer vision technologies to achieve consistent and efficient object detection in real-time. Longitudinal melanonychia, a nail disease often overlooked until it necessitates surgical intervention or long-term treatment, has various causes and potential consequences that are not widely understood. The proposed framework aims to address this gap by enabling early identification, thereby reducing the condition's progression and raising awareness among individuals unfamiliar with nail diseases. The study employs the YOLOv10, a state-of-the-art model for fast and efficient computer vision system development. Manually curated datasets, focused exclusively on the early stages of longitudinal melanonychia, were used for training and validation. The model achieved a mean average precision (mAP) of 98.97% after 100 training epochs, demonstrating high accuracy and reliability. Detection was tested using a laptop webcam, confirming the framework's practical application for live monitoring and early intervention.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".