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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".