<![CDATA[This study aims to apply Convolutional Neural Network (CNN) using DenseNet and Xception to classify fracture in the wrist and hand bones, while utilizing transfer learning to enhance model's performance. Accurate diagnosis and successful treatment of bone fractures depend on early identification, which lowers the likelihood of long-term issues such avascular necrosis or non-union. The research utilized data from two publicly available musculoskeletal radiography datasets and employed deep learning techniques with the Keras framework. DenseNet was selected for wrist image analysis due to its dense connectivity, which preserves information from previous layers, while Xception was chosen for hand bone image analysis because of its ability to identify complex patterns using depthwise separable convolutions. Transfer learning was implemented to accelerate training and improve accuracy. The DenseNet model achieved a test accuracy of 97.5% for wrist classification, while the Xception model reached 92% accuracy for hand bone classification. By tailoring CNN architectures to specific radiographic images and employing transfer learning, this study demonstrates significant potential for improving diagnostic precision in clinical situations. Furthermore, the findings can support medical personnel in detecting bone fractures more efficiently and accurately, ultimately expediting clinical decision-making and improving patient care.]]>
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