Humerus Bone Fracture Detection Utilizing YOLOv4 Algorithm: A Deep Learning Approach

Abstract

The detection of bone fractures is critical for timely medical intervention and patient care. Traditional methods often rely on manual examination of medical images, leading to potential delays and inaccuracies. Leveraging advancements in computer vision, this study investigates the application of the You Only Look Once (YOLO) algorithm for automated bone fracture detection. The detection of humerus bone fractures is critical for orthopedic diagnosis and treatment planning, necessitating accurate and efficient identification to ensure timely clinical intervention. This study proposes a novel approach to humerus bone fracture detection using the You Only Look Once (YOLO) algorithm, a state-of-the-art object detection model in computer vision. Humerus fractures, often resulting from trauma or underlying conditions, require prompt recognition to mitigate complications and optimize patient outcomes. The research outlines the process of dataset collection, preprocessing, model training, and evaluation within the context of YOLO implementation. Furthermore, the paper presents empirical findings regarding the model's precision shedding light on its efficacy in fracture detection. The implications of automated fracture detection systems in enhancing diagnostic efficiency and healthcare delivery, particularly in radiology and orthopedics, are also discussed.

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