Automatic Fracture Image Recognition Based on an Improved Multi-Stream Residual Convolutional Neural Network

This study proposes an automatic fracture image recognition method based on an improved Multi-Stream Residual Convolutional Neural Network (MSR-CNN). Fractures, typically caused by external forces or diseases, are often accompanied by severe pain, swelling, and functional impairment. This is particularly concerning for the elderly, as fractures can lead to prolonged bed rest and multiple complications, significantly affecting their quality of life. Imaging techniques such as Xray, CT, MRI, and ultrasound play a crucial role in fracture diagnosis. However, traditional diagnostic methods heavily rely on the subjective judgment of radiologists, which has inherent limitations. The application of computer-aided diagnosis (CAD) and artificial intelligence technologies has brought about new breakthroughs in medical image recognition. The proposed MSRCNN model combines multi-stream network structures with residual network modules, effectively enhancing the accuracy and efficiency of fracture image recognition. Experimental results demonstrate that the model achieves excellent performance in terms of accuracy, precision, recall, and F1 scores in fracture detection tasks, highlighting its potential for broader applications in medical image analysis.