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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 1285
• Main Authors: Hardalaç, Fırat, Uysal, Fatih, Peker, Ozan, Çiçeklidağ, Murat, Tolunay, Tolga, Tokgöz, Nil, Kutbay, Uğurhan, Demirciler, Boran, Mert, Fatih
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2022; MDPI
• Subjects: Accuracy; artificial intelligence; biomedical image processing; bone fractures; Bones; Classification; Datasets; Deep Learning; fracture detection; Fractures; Humans; Medical equipment; Medical records; object detection; Physiological apparatus; Radiography; Skin; Wrist; Wrist - diagnostic imaging; Wrist Joint; X-Rays; bone fractures; deep learning; transfer learning; fracture detection; biomedical image processing; object detection; X-ray; wrist; artificial intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22031285

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 patient’s medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray 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 Hospital’s dataset of wrist X-ray 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.