The Future of Orthopedic Care: High-Accuracy Bone Fracture Detection with CNNs

Bone fractures are a severe health issue and affect millions of people across the globe. Their diagnosis must thus be timely and correct for appropriate treatment. Conventional diagnosis of bone fracture is mainly done through manual examination of radiographic evidence on X-rays, and hence, it suffers from variability. To address this challenge, this paper proposes a deep learning-based approach for classifying bone fractures using convolutional neural networks. The training and validation of our model were performed on a dataset of 399 labeled X-ray images aggregated into classes “fracture” and “non-fracture”. The proposed CNN model demonstrated excellent performance, with results up to $98 \%$ in overall accuracy. Based on the report of classification, outcomes for the non-fracture class were: precision is 0.99, recall is 0.97, F1-score is 0.98, support is 199; for the class of fracture: precision is 0.97, recall is 0.99, F1-score is 0.98, support is 200; and the average for macro and weighted for precision, recall, and F1-score came out to be 0.98 for both classes, indicating the model’s ability and robustness. These results are evident for the great potential of CNN-based models in automating the detection of bone fractures to enhance the diagnostic process and probably improve patient outcomes through quicker, more accurate diagnosis. In the future, this research work will further explore the implementation of this model in clinical setups and estimate the performances on larger, more diverse datasets.