Intelligent Bone Fracture Detection Using Deep Learning and Hybrid Ensemble Models for Enhanced Medical Diagnosis

Abstract

Patient healing success relies on having instant accurate diagnosis of bone fracture since it enables proper medical treatment. X-ray images are interpreted by human technicians manually to make diagnoses during reading sessions but the process has long time periods that create possible human interpretation mistakes. An AI system that integrates deep learning with Random Forest and Logistic Regression offers a more accurate fracture diagnosis system with improved medical detection capability and accuracy. In cases of under diagnosis where bones are hard to distinguish the deep learning model performs accurate fracture detection through the examination of complex X-ray features. Random Forest serves as an improvement tool in model performance based on its ability to deal with images of mixed quality in the various patient conditions. The use of Logistic Regression helps medical professionals identify pertinent factors leading to fractures and provides valuable clinical decision-making tips by doctors. Standardized medical testing and quicker accurate clinical assessment become feasible with a diagnostic combination strategy that enhances accuracy by fewer false positives.