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This study presents a YOLO11-driven deep learning approach to enhance the detection and visualization of wrist fractures in X-ray images, addressing the challenges posed by a shortage of radiologists and the demand for accurate diagnoses. The proposed model integrates attention mechanisms with a Residual Network to improve feature extraction, achieving state-of-the-art performance with a mean average precision (mAP50) of 0.651. Additionally, a web-based interface is developed to assist surgeons in identifyi

Author

m19904779256

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EN