Automatic bone fracture detection in x-ray images using deep learning
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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 from traffic accidents and falls. The situation becomes worse year on year due to a rising life expectancy, and thus an increasing number of the ageing population who are more prone to fractures. 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 the open-source dataset. We developed a model for the localization of 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.