Automated detection of pubic rami fractures in X-ray images using pretrained Convolutional Neural Networks

Abstract

Pubic rami fractures are common in the elderly, often resulting from low-energy trauma, and can be very challenging to diagnose because they are subtle on X-rays. It is crucial to accurately identify this condition at the right time to prevent complications such as immobilisation and chronic pain. The proposed study involves a pretrained convolutional neural network (CNN) based automated detection system for pubic rami fractures in X-ray images, which will be collected from public hospitals in Sri Lanka. The dataset, consisting of 2,000 X-ray images labelled as fractured or non-fractured, was preprocessed and augmented to improve the model’s robustness. Four pretrained CNN models, such as ResNet-50, ResNet-101, EfficientNet-B0, and EfficientNetV2, were fine-tuned for binary classification of pubic rami fractures. The ResNet-101 model showed the best performance, achieving an accuracy of 82%, precision of 0.84, recall of 0.82, and F1 score of 0.83, outperforming other models and even the custom CNN baseline. These results highlight the potential of pretrained CNNs to improve detection accuracy through transfer learning, especially given the scarcity of medical image data. The system could assist clinicians in early diagnosis, reduce diagnostic errors, and streamline clinical workflows. However, challenges such as unbalanced class representation and the subtlety of fracture signs remain, indicating that larger and more diverse datasets, along with more advanced augmentation methods, will be necessary in future studies.