A Novel Deep Learning Model for Recognition of Bone Fractures in X-Ray Images

Fracture detection in X-ray images is a significant task in medical diagnosis, and its accurate identification with speed has the potential to bring improvement in patient outcomes. With traditional approaches, detection depends on manual examination, which is always time-consuming and susceptible to human error. This paper presents an efficient deep-learning model for detecting fractures from X-ray images intended for speed and improved accuracy in automated diagnosis. Efficient deep-learning architectures are utilized in this model, such as lightweight CNNs, a balance between accuracy and computational efficiency so that the proposed system finds real time medical uses. The results of the experiments show that the approach achieves better performance in terms of both accuracy and speed of inference compared to traditional methods, thus being useful even in emergencies or remote consultations. It has the potential to serve as a very valuable tool to enhance the capabilities of radiologists by smoothing their workflow and enhancing the patient experience. Matrices like accuracy, precision, f1-score, and recall values were used to analysis a model's feasibility using fracture X-ray image datasets. Experimental results indicate that our proposed method accomplished an average accuracy of 97.27 on the dataset, a maximum recall rate of 0.97, and the highest f1-score of 0.97. The introduced model has great classification accuracy and excellent generalization performance.