<![CDATA[Bone fracture is a common medical condition that often affects elderly populations or individuals with degenerative diseases such as osteoporosis. Manual classification of fractures from X-ray images presents diagnostic challenges due to visual complexity and interobserver variability. In this study, we implemented and compared Deep Neural Network (DNN) and Convolutional Neural Network (CNN) architectures to classify bone fractures from radiographic images. The dataset consisted of 4099 X-ray images divided into fractured and non-fractured categories. Each model was trained using preprocessed and augmented data and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the CNN model achieved better classification performance, with an accuracy of 80% and balanced class scores. In contrast, the DNN model showed poor generalization and strong bias toward the fractured class, yielding only 51% accuracy. This study concludes that CNN are more suitable for bone fracture classification tasks due to their superior ability to extract spatial features and generalize across categories.]]>
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