| dc.description.abstract | Bone fractures are a leading cause of morbidity and mortality worldwide. In Uganda, statistics for the prevalence of bone fractures
are unknown, although anecdotal evidence points to a high incidence, mostly arising out of traffic accidents and falls. To reduce the debilitating effects of these fractures and improve quality of life, it is important that the fractures are accurately diagnosed early on. X-ray imaging is the most common imaging modality for fracture diagnosis in Uganda, but its manual interpretation is usually error-prone, potentially leading to missed diagnoses.
To address this challenge, this project aimed at developing an automated bone fracture detection system for efficient diagnosis, utilizing a deep learning approach. Images of fractured bones were obtained from Roboflow and an open-source dataset. We developed a model for bone fracture detection, utilizing the YOLOv5 architecture which achieved a
mean average precision of 86.6% and outperformed others such as EfficientDet and Detectron2. Our model, when integrated into a clinical decision support system, is potentially a promising approach to improve clinical outcomes
based on accurate and efficient bone fracture detection from x-ray data. | en_US |
