Maxillofacial Fracture Detection Using Transfer Learning Models : A Review

Early detection and treatment of face bone fractures reduce long-term problems. Fracture identification needs CT scan interpretation, but there aren't enough experts. To address these issues, researchers are classifying identifying objects. Categorization-based studies can't pinpoint fractures. Proposed Study Convolutional neural networks with transfer learning may detect maxillofacial scans were utilized to retrain fine-tune a convolutional network trained on non-medical images categorize incoming CTs as "Positive" or "Negative." Model training employed fractogram data. If two successive slices had 95% fracture risk, the patient fracture. In terms sensitivity/person for facial fractures, recommended strategy beat machine model. The approach minimize physicians' effort in CT. Even though technology fully replace radiologist, technique be helpful. It reduces human error, diagnostic delays, hospitalization costs.