Efficient YOLOv11-Based Approach with Dual-Path Feature Learning for Automated Detection of Limb Fractures
Bibliographic record
Abstract
The proper detection of limb fractures by use of radiographic images is crucial to medical institutions that have low resources. The study proposes a new deep learning platform named YOLOv11 that enhances the speed and accuracy of bone fracture tailoring in medical images. The YOLOv11 system has identified the weaknesses of the earlier versions of the YOLO version by having a dual-path solution to feature extraction and the mechanism of improved attention and gradient consistency refinement. Two publicly available radiograph databases were utilised by the research to construct a hybrid dataset comprising 4,739 labelled images and 1,030 X-rays of Gujranwala Medical College Hospital to be used diversely and practically. The evaluation results showed YOLOv11 achieved 0.89 precision and 0.81 mAP@0.5 and 0.55 mAP@0.5–0.95 while outperforming YOLOv8 and YOLOv10 and Faster R-CNN in both performance and speed performance. The model showed excellent performance on local clinical data and it processed images at 62 FPS in real-time. YOLOv11 serves as an essential tool for AI-based radiology support in orthopedic trauma treatment because it provides high diagnostic performance with minimal system requirements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".