A Novel Revolutionizing Prediction of Bone Fracture using Deep Learning Techniques

Bone Fractures is essential for prompt and effective patient care. The methodology Convolutional Neural Networks (CNNs), Region-based Convolutional Neural Networks (RCNNs), and YOLOv10 are three deep learning models that are used in this study to investigate automated fracture identification in medical imaging. With an astounding 98% accuracy rate, CNNs demonstrate their capacity to directly extract complex characteristics from unprocessed pixel input for image categorization. Additionally, with 92% and 90% accuracy, respectively, RCNNs and YOLOv10 produce results that are competitive. A sizable dataset of X-ray pictures with annotated fracture markers is used to train and evaluate the models. To increase generality and resilience, preprocessing methods like image augmentation and normalization are used. While pointing out the possibilities of alternative techniques, experimental results show CNNs' greater precision for fracture diagnosis. Automated healthcare diagnosis is advanced by this work. by showcasing dependable and effective deep learning methods for detecting bone fractures, opening the door to better clinical judgment.