# Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models

arXiv: https://arxiv.org/abs/2111.07355 · HTML: https://mlsanity.com/paper/2111.07355/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 14 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://mlsanity.com/paper/2007.08103/) (2020)
- [Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://mlsanity.com/paper/1903.00621/) (2019)
- [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://mlsanity.com/paper/2004.06002/) (2020)
- [Side-Aware Boundary Localization for More Precise Object Detection](https://mlsanity.com/paper/1912.04260/) (2019)
- [Localization Recall Precision (LRP): A New Performance Metric for Object Detection](https://mlsanity.com/paper/1807.01696/) (2018)
- [Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation](https://mlsanity.com/paper/1910.05370/) (2019)
- [One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks](https://mlsanity.com/paper/2011.10772/) (2020)
- [GILBO: One Metric to Measure Them All](https://mlsanity.com/paper/1802.04874/) (2018)

## Built on by (most cited first, 10 of 10)

- [Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models](https://mlsanity.com/paper/2405.20775/) (2024)
- [Enhancing Wrist Fracture Detection with YOLO](https://mlsanity.com/paper/2407.12597/) (2024)
- [Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge](https://mlsanity.com/paper/2504.02382/) (2025)
- [DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images](https://mlsanity.com/paper/2308.12727/) (2023)
- [Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset](https://mlsanity.com/paper/2408.13542/) (2024)
- [Semi-supervised object detection based on single-stage detector for thighbone fracture localization](https://mlsanity.com/paper/2210.10998/) (2022)
- [Enhancement of theColor Image Compression Using a New Algorithm based on Discrete Hermite Wavelet Transform](https://mlsanity.com/paper/2303.13175/) (2023)
- [Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset](https://mlsanity.com/paper/2412.13884/) (2024)
- [A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis](https://mlsanity.com/paper/2505.14716/) (2025)
- [FracDetNet: Advanced Fracture Detection via Dual-Focus Attention and Multi-scale Calibration in Medical X-ray Imaging](https://mlsanity.com/paper/2509.23416/) (2025)

## Abstract (arXiv)

Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patients medical records and physical examination. The aim of this study is to perform fracture detection by use of deep learning on wrist Xray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R_CNN, FSAF, Faster R_CNN, Dynamic R_CNN and DCN deep learning based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospitals dataset of wrist Xray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, wrist fracture detection_combo (WFD_C). From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor. Code is available at https://github.com/fatihuysal88/wrist-d