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

TL;DR

This study applies various deep learning object detection models to wrist X-ray images to accurately identify fractures, developing an ensemble approach that achieves high detection precision to assist physicians in emergency diagnosis.

Contribution

Introduces a novel ensemble deep learning model for wrist fracture detection that outperforms individual models on a real hospital dataset.

Findings

Highest AP50 of 0.8639 achieved by the ensemble model

20 different fracture detection procedures evaluated

Ensemble model improves detection accuracy over individual models

Abstract

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