Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm

Abstract

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we use data augmentation to improve the model performance of YOLOv8 algorithm (the latest version of You Only Look Once) on a pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX), which is a public dataset. The experimental results show that our model has reached the state-of-the-art (SOTA) mean average precision (mAP 50). Specifically, mAP 50 of our model is 0.638, which is significantly higher than the 0.634 and 0.636 of the improved YOLOv7 and original YOLOv8 models. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery.

Figure 1

Flowchart of the model training, validation and testing on the dataset. The extended training set is used to double the number of X-ray images by data augmentation.

Figure 2

The architecture of YOLOv8 algorithm, which is divided into four parts, including backbone, neck, head, and loss.

Figure 3

Examples of pediatric wrist X-ray images using data augmentation. (a) the original images, (b) the adjusted images.

Figure 4

Detailed illustration of YOLOv8 model architecture. The Backbone, Neck, and Head are the three parts of our model, and C2f, ConvModule, DarknetBottleneck, and SPPF are modules.

Figure 5

Detailed illustration of the validation at the input image size of 1024, (a) is our model, and (b) is YOLOv8 model.

Figure 6

Examples of pediatric wrist fracture detection on X-ray images. (a) manually labeled images, (b) predicted images.

Figure 7

Example of using the application “Fracture Detection with YOLOv8 Application” on macOS operating system .