# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 1 Introduction  
Bone fractures are a typical emergency in hospital settings, especially in pediatric patients, who frequently sustain wrist or arm injuries [1]. In Great Britain, approximately one-third of children experience at least one fracture before reaching 17 years of age [2]. Accurate diagnosis and treatment are critical to prevent long-term complications. Radiology, the medical specialty that uses imaging to diagnose and treat diseases within the body, plays a crucial role in emergency medicine, particularly in detecting bone fractures. Fractures can be classified into four types: angle fractures (with some degree of bone angulation), typical fractures (aligned bone pieces), line fractures (thin fractures without significant displacement), and messed-up angle fractures (involving fracture, displacement, or rotational displacement) [3, 4]. Treatment varies depending on the fracture type and location, making accurate and rapid identification essential. To diagnose fractures, radiologists utilize different imaging devices, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-rays [5], with X-rays being the most used due to their cost-effectiveness and versatility, while MRI has a superior detection rate [6]. However, the effectiveness of medical imaging is limited by the skill of the surgeons analyzing the scans. In regions with a shortage of experienced surgeons, patient safety can be compromised [7]. Studies concluded that a significant portion of patients' X-rays were misinterpreted in African hospitals [8]. To combat this, integrating computer vision into radiology has been proposed as a promising solution to improve patient care significantly. Fracture detection has been a popular topic as the complexity of its data and its requirement for accuracy challenge any object detection algorithm. Recent advancements in deep learning and computer vision offer promising solutions to these challenges. Object detection models, particularly those based on convolutional neural networks (CNNs), have shown great potential in medical image analysis [9]. Among these, the YOLO (You Only Look Once) [10] series of algorithms is particularly well-suited for real-time applications, due to its balance between accuracy and inference speed [11]. Deep learning models, such as R-CNN [12], Fast R-CNN [13], and the YOLO series, arecommonly used in computer vision for efficient and accurate image classification. These models are divided into one-stage and two-stage algorithms, with two-stage algorithms having distinct stages for producing regions and searching for features. In contrast, one-stage algorithms generate regions and search for features simultaneously. Attention-based models, like transformers, were initially developed for natural language processing and language translation [14] but have started to gain popularity in computer vision [15]. A study on the Dilated Convolutional Feature Pyramid Network (DCFPN) [16] was trained to achieve an Average Precision (AP) of 82.1% on a dataset of thigh fracture X-ray images [17]. Further, the Regional Convolutional Neural Network (R-CNN) model, alongside image preprocessing, was used to obtain an AP of 62% accuracy [18] on the Musculoskeletal Radiograph (MURA) [19] dataset of nearly 4000 arm fractures. A Faster Region with Convolutional Neural Network (Faster R-CNN) model [20] was able to achieve an 88.4% mAP score [21], from a dataset of 1,052 images, 526 corresponding to various fractures. Their results showed great potential for object detection algorithms in the medical field; however, more accurate, two-stage convolution neural network approaches tend to be slow and hardware-intensive for practical uses.