New Era in Fracture Diagnosis using Deep Learning's Role in Precision Prediction

The critical problem of using advanced image analysis techniques to diagnose and classify bone fractures is the main topic of this work. Bone fractures are common and have a significant effect on patient outcomes, hence timely diagnosis is essential for proper treatment. Identification done quickly is essential to reducing issues and maximising general recovery. A dataset of 4906 X-ray images classified as either broken or not is used in the study. One model it uses is a Sequential Convolutional Neural Network (CNN). With 4099 images for training, 401 images for testing, and 406 images for validation, the CNN model classifies fractures with an astounding 98% accuracy. The method shows how deep learning in medical imaging may be used to diagnose fractures accurately and quickly, therefore enhancing healthcare outcomes.