Bone Fracture Detection Using Machine Learning

Bone fractures are among the most common traumatic injuries, requiring accurate and timely diagnosis for effective treatment. Conventional diagnostic techniques, such as manual interpretation of X-ray images by radiologists, are subject to human error and limited by workload and experience. In recent years, machine learning (ML) has emerged as a powerful tool to enhance diagnostic accuracy and efficiency in medical imaging. This paper presents a machine learning-based approach for automated bone fracture detection using convolutional neural networks . A curated dataset of annotated X-ray images was used to train and evaluate the model. The proposed system achieved an accuracy , precision and recall of demonstrating its potential for real-world clinical applications. The integration of ML into diagnostic workflows can support radiologists in identifying fractures more reliably, reduce diagnostic delays, and improve patient outcomes. Future work will focus on extending the model to 3D imaging modalities and incorporating multi-class classification for different types of fractures.