Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.13408 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918096910942208 |
|---|---|
| author | M, Hemanth Kumar M, Karthika M, Saianiruth Venugopal, Vasanthakumar D, Anandakumar Ezhumalai, Revathi K, Charulatha J, Kishore Kumar G, Dayana Sivasailam, Kalyan Subramanian, Bargava |
| author_facet | M, Hemanth Kumar M, Karthika M, Saianiruth Venugopal, Vasanthakumar D, Anandakumar Ezhumalai, Revathi K, Charulatha J, Kishore Kumar G, Dayana Sivasailam, Kalyan Subramanian, Bargava |
| contents | Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radiologists. AI-driven tools offer a scalable way to assist early detection and reduce diagnostic delays. We address this gap through a dedicated AI system for shoulder radiographs. Methods: We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays. Architectures include Faster R-CNN (ResNet50-FPN, ResNeXt), EfficientDet, and RF-DETR. To enhance detection, we applied bounding box and classification-level ensemble techniques such as Soft-NMS, WBF, and NMW fusion. Results: The NMW ensemble achieved 95.5% accuracy and an F1-score of 0.9610, outperforming individual models across all key metrics. It demonstrated strong recall and localization precision, confirming its effectiveness for clinical fracture detection in shoulder X-rays. Conclusion: The results show ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance. The model's accuracy and deployment readiness position it well for integration into real-time diagnostic workflows. The current model is limited to binary fracture detection, reflecting its design for rapid screening and triage support rather than detailed orthopedic classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs M, Hemanth Kumar M, Karthika M, Saianiruth Venugopal, Vasanthakumar D, Anandakumar Ezhumalai, Revathi K, Charulatha J, Kishore Kumar G, Dayana Sivasailam, Kalyan Subramanian, Bargava Computer Vision and Pattern Recognition Artificial Intelligence 68T07 I.2.10 Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radiologists. AI-driven tools offer a scalable way to assist early detection and reduce diagnostic delays. We address this gap through a dedicated AI system for shoulder radiographs. Methods: We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays. Architectures include Faster R-CNN (ResNet50-FPN, ResNeXt), EfficientDet, and RF-DETR. To enhance detection, we applied bounding box and classification-level ensemble techniques such as Soft-NMS, WBF, and NMW fusion. Results: The NMW ensemble achieved 95.5% accuracy and an F1-score of 0.9610, outperforming individual models across all key metrics. It demonstrated strong recall and localization precision, confirming its effectiveness for clinical fracture detection in shoulder X-rays. Conclusion: The results show ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance. The model's accuracy and deployment readiness position it well for integration into real-time diagnostic workflows. The current model is limited to binary fracture detection, reflecting its design for rapid screening and triage support rather than detailed orthopedic classification. |
| title | A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07 I.2.10 |
| url | https://arxiv.org/abs/2507.13408 |