Bone Fracture Detection Using Hybrid EfficientNet-B0 and ResNet50 with SVM: A Comparative Performance Analysis
Bibliographic record
Abstract
The accurate identification of osseous fractures is crucial for precise medical diagnoses and treatment planning.This study introduces a new hybrid classification approach, integrating EfficientNet-B0 and ResNet50 deep learning models with an SVM classifier, surpassing traditional versions.Leveraging pre-trained feature extractors for EfficientNet-B0 and ResNet50, the proposed method achieves a test accuracy of 98.01% and a recall of 0.99 for fractured cases with EfficientNet-B0+SVM, while reducing runtime to 20.44 minutes.ResNet50 + SVM also improved accuracy from 80.05% to 96.41% with a runtime of 38.47 minutes, compared to 83.96 minutes standalone.This hybrid approach demonstrates significant enhancements in accuracy and efficiency, positioning it as a promising tool for clinical bone fracture detection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".