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. The situation becomes worse year on year due to a rising life expectancy, and thus an increasing number of the aging population who are more prone to fractures. To reduce the debilitating e↵ects 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 the efficient diagnosis, utilizing a deep learning approach. Images of fractured bones were obtained from Roboflow and the open-source dataset. We developed a model for localization of the bone fractures, utilizing the YOLOv5 architecture. Our best model achieved a mean average precision of 85.6%. Comparison with alternative approaches such as EfficientDet, and Detectron2 reveals the superior performance of our model. 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.

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