Automatic Detection of Bone Fracture Using Deep Neural Models

Abstract

Deep learning’s latest improvements have considerably affected medical imaging, especially when it comes to the diagnosis of bone breaks. In this study, we introduce MedNet, a particularly strong framework for fracture detection. MedNet uses a greatly modified ResNet-50 architecture greatly improved with diverse data augmentation techniques. The model was trained on a combination of the MURA dataset and several other available medical images, guaranteeing a diverse representation of multiple fracture cases. MedNet achieves greater than 98% accuracy when training and near 99% accuracy when validating, with AUC approaching 0.999, which shows its effectiveness and its avoidance of overfitting. MedNet performs better than other configurations such as VGG16 (93.4%), DenseNet121 (95.2%), InceptionV3 (91.6%), MobileNetV2 (90.8%), and EfficientNetB0 (96.1%). These quite promising results highlight the potential of AI-driven methodologies to substantially improve diagnostic precision along with efficiency in clinical settings, thereby greatly assisting radiologists in making exceptionally timely as well as accurate decisions.

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