Synthetic Image Generation for Improved Detection of Bone Defects Using GAN

Automated Fractures of Bone Detection in orthopedic radiographs is crucial in assisting doctors to correctly and properly diagnose fractures. For this assignment, we investigate a Generative Adversarial Networks (GAN)-based solution that employs sophisticated artificial intelligence models capable of producing realistic photos while detecting anomalies. It display efficiency of GAN-based models in boosting fracture detection performance by applying multiple assessment criteria, such precision, accuracy, and F1-score, and detection speed, through thorough testing and evaluation of musculoskeletal radiograph datasets (MURA). GANs function by retraining networks of generators to produce artificial pictures and a network of discriminators. This antagonistic process encourages the formation of realistic radiography with fractures, enabling precise and automatic detection. Our proposed approach makes use of GANs' capability to produce artificial pictures with cracks and recognize, abnormal patterns, thereby improving fracture diagnosis These findings highlight the prospect of introducing GANs into automated evaluation, which might assist with develop fracture detection systems in orthopaedics . Our findings advance fracture detection approaches, paving the way for improved and more accurate orthopaedic diagnostic tools.