Automated Bone Fracture Detection using YOLOv8

In medical diagnostics, detection of bone fracture is very crucial. It require time and accurate treatment of that injuries. Previous methods for bone fracture detection are X-ray in which analyzes the fracture by the help of radiations. It is time consuming and prone to human error. To address these limitations we used an automated approach for detection of bone fracture is yolov8. It is deep learning model for real time object detection. Its well suited for medical imagining because its architecture give more accuracy and speed. Our results show YOLOv8 achieve high accuracy and speed from the traditional methods. It is also more reliable. Since it is less time consuming and give the consistent results so it can be used in healthcare. It is possible that its efficiency can be enhance, minimize errors. This study shows the potential of deep learning model like YOLOv8 in diagnostics and medical imaging particularly in bone fracture detection