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Fracatlas dataset, showing scans containing various parts of the arm, leg, waist and shoulder. Each fracture instance has its own mask and bounding box, and the scans also have a global label for the classification task, which is set to “fractured”.
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The WCAY (weighted channel attention YOLO) model, which is meticulously crafted to identify fracture features across diverse X-ray image sites, is presented herein. This model integrates novel core operators and an innovative attention mechanism to enhance its efficacy. Initially, leveraging the benefits of dynamic snake convolution (DSConv), which...
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... Feature fusion techniques have also played a critical role in improving detection performance. Chen et al. (2024) demonstrated that integrating AC-BiFPN into YOLO improved detection accuracy to 94% on radiographs of varying resolutions. Lu et al. (2022) further extended this approach by employing adaptive anomaly detection modules alongside AC-BiFPN, significantly reducing false positives in high-density medical images. ...
This paper proposes a novel deep learning-based approach for detecting pediatric wrist fractures in radiographs. Our method integrates AC-BiFPN for efficient multi-scale feature fusion and SimAM to emphasize clinically relevant image features, enhancing real-time object detection using YOLOv10. Additionally, we employ the WIoU loss function to improve the model’s generalization capability by minimizing both false positives and, more critically, false negatives. The proposed model was evaluated on the GRAZPEDWRI-DX dataset, comprising 20,327 annotated pediatric wrist radiographs. Our approach achieved significant performance improvements, with a precision of 97.4%, recall of 95.5%, and of 88.5%. Notably, the model demonstrated a strong ability to detect subtle and complex fractures, which are often missed by conventional diagnostic methods. Furthermore, the system exhibited robustness across diverse clinical scenarios while maintaining computational efficiency, making it suitable for real-time deployment in emergency departments. These results suggest that our model not only surpasses traditional fracture detection techniques but also provides a reliable and efficient tool to assist radiologists in pediatric emergency care.
