FracturePro: Automated Bone Fracture Identification and Classification Using an Ensemble Deep Learning Approach

In modern medical diagnostics, machine learning (ML) has emerged as a transformative tool, reshaping healthcare practices. This study investigates the application of ML for the timely and accurate detection of bone fractures, a critical aspect of orthopedic care. Delayed diagnosis can result in complications such as malunion or non-union, underscoring the need for real-time, precise identification of fracture types and locations. While X-ray interpretation relies heavily on the expertise of medical professionals, poor image quality often poses significant challenges. To address these issues, this research introduces a novel ML-based model for fracture segmentation and classification. The model leverages advanced architectures, including ResNet, DenseNet, and U-Net, to perform fracture segmentation and classification with high accuracy. Additionally, image enhancement and preprocessing techniques are incorporated to mitigate the limitations posed by low-quality radiographic images. The findings highlight the potential of ML in improving diagnostic precision and efficiency, ultimately enhancing patient outcomes in orthopedic care.