Early and accurate detection of bone fractures is essential for effective medical diagnosis and treatment planning. Traditional radiographic interpretation is often time-consuming and dependent on the expertise of radiologists, which can lead to human error and delayed diagnosis. This study proposes a DenseNet-based deep learning system for automated fracture detection using medical imaging data such as X-rays. DenseNet, or Densely Connected Convolutional Network, enhances feature propagation and mitigates the vanishing gradient problem by connecting each layer to every other layer in a feed-forward manner. The proposed model is trained and validated on a curated dataset of labeled bone images, where preprocessing techniques such as image normalization, augmentation, and noise reduction are applied to improve model robustness. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, are used to assess the system’s diagnostic efficiency. Experimental results demonstrate that the DenseNet-based model outperforms conventional convolutional neural networks (CNNs) in both accuracy and convergence speed. The system provides a reliable and automated solution for assisting clinicians in fracture detection, reducing diagnostic time, and enhancing decision-making in emergency care.
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