# Pediatric Wrist Fracture Detection in X-rays via YOLOv10 Algorithm and Dual Label Assignment System

arXiv: https://arxiv.org/abs/2407.15689 · HTML: https://mlsanity.com/paper/2407.15689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm](https://mlsanity.com/paper/2304.05071/) (2023)
- [YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images](https://mlsanity.com/paper/2403.11249/) (2024)
- [YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection](https://mlsanity.com/paper/2402.09329/) (2024)
- [Enhancing Wrist Fracture Detection with YOLO](https://mlsanity.com/paper/2407.12597/) (2024)
- [DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images](https://mlsanity.com/paper/2308.12727/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset](https://mlsanity.com/paper/2408.13542/) (2024)

## Abstract (arXiv)

Wrist fractures are highly prevalent among children and can significantly impact their daily activities, such as attending school, participating in sports, and performing basic self-care tasks. If not treated properly, these fractures can result in chronic pain, reduced wrist functionality, and other long-term complications. Recently, advancements in object detection have shown promise in enhancing fracture detection, with systems achieving accuracy comparable to, or even surpassing, that of human radiologists. The YOLO series, in particular, has demonstrated notable success in this domain. This study is the first to provide a thorough evaluation of various YOLOv10 variants to assess their performance in detecting pediatric wrist fractures using the GRAZPEDWRI-DX dataset. It investigates how changes in model complexity, scaling the architecture, and implementing a dual-label assignment strategy can enhance detection performance. Experimental results indicate that our trained model achieved mean average precision (mAP@50-95) of 51.9% surpassing the current YOLOv9 benchmark of 43.3% on this dataset. This represents an improvement of 8.6%. The implementation code is publicly available at https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection