Bone Fracture Detection in X-ray images using Convolutional Neural Networks

Abstract

Bone fractures are a prevalent form of musculoskeletal injury that require timely and accurate diagnosis for effective treatment. Radiographic imaging, particularly X-ray analysis, remains the primary diagnostic tool. However, manual interpretation by radiologists is subject to human error, fatigue, and variability in judgment. This research presents a deep learning-based approach for the automated detection of bone fractures in X-ray images using Convolutional Neural Networks (CNNs). The proposed system is trained on a publicly available dataset comprising labeled images of fractured and non-fractured bones. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to improve model robustness and generalization. The CNN architecture was designed and optimized to learn distinguishing features from input images without manual feature engineering. The model achieved a high classification accuracy of over 93% on test data, demonstrating strong potential for assisting clinical diagnosis. Evaluation metrics including precision, recall, and F1-score indicate that the model can reliably differentiate between fractured and healthy bone structures. The system is scalable, cost-effective, and suitable for integration into computer-aided diagnostic tools, particularly in resource-limited settings. This study contributes toward the development of intelligent diagnostic systems that can support healthcare professionals by reducing diagnostic delays and enhancing patient outcomes.

Keywords: X-ray images, Convolutional Neural Networks (CNN), Deep Learning, Computer-aided detection (CAD).