DETECTION AND CLASSIFICATION OF FEMORAL NECK FRACTURE USING YOLOV8

Abstract

Femoral neck fractures are considered to be one of the most challenging orthopedic conditions because of the technical difficulties in the management of these injuries and possible complications, including nonunion and avascular necrosis. These fractures are common in elderly patients and they mostly occur due to low energy trauma such as falls. Early diagnosis and identification of femoral neck fractures are critical to proper clinical management and in reducing complications. This study employs the YOLOv8 model, which is a recent advancement in object detection, to detect and classify femoral neck fractures in X-ray images. The YOLOv8 model shows impressive results, with the mean Average Precision mAP50 of 97. 9%, a precision of 93. 5%, and a mAP50-95 of 62. 5%. Our proposed system encompasses several stages: data collecting, data preprocessing, model training, model validation, and model deployment. In the process of data preprocessing several data augmentation methods was performed to improve the model’s resilience. The YOLOv8 model was then trained with this dataset and further rigorous testing conducted to determine the efficiency of the model. The results show that the proposed model has great potential for the automatic detection and classification of femoral neck fractures, which will be helpful for radiologists. When implemented in clinical environments, this system may increase diagnostic accuracy, decrease the workload, and consequently, benefit patients