Analyzing the Performance of Convolutional Neural Networks and Transformer Models in Automated Bone Fracture Detection

The most significant component of the skeletal and muscular system, whose function is vital to human existence, are the bones. Breaking a bone might occur from a specific hit or from a violent rearward movement. In this study, bone fracture detection was performed using convolutional neural network (CNN) based models, Faster R-CNN and RetinaNet, as well as a transformer-based model, DETR (Detection Transformer). A detailed investigation was conducted using different backbone networks for each model. This study's primary contributions are a methodical assessment of the performance variations between CNN and transformer designs. Models trained on an open-source dataset consisting of 5145 images were tested on 750 test images. According to the results, the RetinaNet/ResNet101 model exhibited superior performance with a 0.901 mAP50 ratio compared to other models. The obtained results show promising outcomes that the trained models could be utilized in computer-aided diagnosis (CAD) systems.