<![CDATA[Fracture detection in radiographic images is a critical task in orthopaedic diagnostics, often requiring timely and accurate interpretation by medical professionals. However, manual evaluation of X-rays is time-consuming and prone to subjective bias. This study proposes an automated deep learning approach for binary classification of bone fractures using a pre-trained ResNet18 architecture. The model was trained and validated on a multi-region X-ray dataset consisting of 10,580 images categorized into fractured and non-fractured classes. To improve generalization, data augmentation techniques such as rotation and horizontal flipping were applied during pre-processing. The final model achieved a validation accuracy of 97.59%, with high true positive and true negative rates as confirmed by the confusion matrix analysis. The results demonstrate the effectiveness of transfer learning in handling radiographic image classification tasks while maintaining computational efficiency. This research contributes to the development of reliable and scalable computer-aided diagnostic tools that can support clinical decision-making, especially in environments with limited resources.]]>
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