Bone fractures represent one of the most prevalent medical conditions resulting from traumatic events such as accidents, falls, and sports-related injuries. Accurate and timely detection of fractures is critical for effective treatment and patient recovery. Conventional diagnostic methods rely heavily on manual interpretation of X-ray images by radiologists, which can be time-consuming and susceptible to human error, particularly in cases involving subtle fracture patterns or large volumes of medical data. This paper presents the design and implementation of an automated Bone Fracture Detection System utilizing deep learning and computer vision techniques. The proposed system employs Convolutional Neural Networks (CNNs) for feature extraction and classification, along with advanced architectures such as VGGNet for image classification and Faster R-CNN for fracture localization. The system is trained on a dataset comprising both fractured and non-fractured X-ray images, enabling it to learn complex visual patterns associated with bone abnormalities. The implementation is carried out using Python and integrates powerful libraries including TensorFlow, Keras, OpenCV, and NumPy. A user-friendly web-based interface is developed using Streamlit, allowing users to upload X-ray images and obtain real-time predictions. The system processes input images through preprocessing techniques such as normalization, resizing, and augmentation before performing classification and detection tasks. Experimental results demonstrate that the proposed system achieves high accuracy in detecting bone fractures, with reliable performance across varied image conditions. The integration of classification and object detection models enables both identification and localization of fractures, enhancing the interpretability of results. The system significantly reduces diagnostic time and supports healthcare professionals in decision-making processes. This work highlights the potential of deep learning in medical image analysis and provides a scalable, efficient, and cost-effective solution for automated fracture detection. The proposed system can serve as a valuable decision-support tool, particularly in resource-constrained healthcare environments.
Kumar et al. (Thu,) studied this question.