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

Authors : Ece Bingöl, Semih Demirel, Ataberk Urfalı, Ömer Faruk Bozkır, Azer Çelikten, Abdulkadir Budak, Hakan Karataş

Pages : 64-71

Doi:10.18586/msufbd.1440119

View : 111 | Download : 126

Publication Date : 2024-12-30

Article Type : Research Paper

Abstract :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.
Keywords : Kemik kırığı, vision transformer, DETR, Faster R-CNN, RetinaNet

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