# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 2 Model Architecture  
The YOLOv8 architecture has three main components: the backbone, the neck, and the head. Figure 1 provides a detailed overview of each part and explains the design principles and functionalities of the various modules. The backbone of the YOLOv8 model is essential for feature extraction from input images. It employs a Cross-Stage Partial (CSP) architecture, which divides the feature map into two paths [33]. The first path undergoes convolution operations, while the second path concatenates with the output from the first, enhancing learning capabilities and reducing computational overhead. The CSP technique enhances the gradient flow during the training process by creating shortcuts for gradients, reducing the vanishing gradient problem, which is an issue that occurs during deep neural network training where the gradients of the loss function become extremely small as they are backpropagated through the network layers, making the gradients too small for effective learning. Additionally, CSP allows some maps to bypass layers, which helps preserve unique features. This is important because those features contribute to the model's ability to learn and recognize different patterns. If all the feature maps are processed the same way, the model can miss important variations that could improve the performance. A significant change in YOLOv8 is the introduction of the C2f module, which merges concepts from the C3 module and Efficient Layer Aggregation Networks (ELAN) used in YOLOv7 [34]. The C2f module comprises two ConvModules and a series of DarknetBottleNeck units linked through split and concatenate operations. Each ConvModule includes convolution, batch normalization, and SiLU activation functions. This module enhances feature representation by enabling cross-stage connections that combine shallow and deep features. Cross-stage connectivity promotes partial feature reuse, ensuring critical information is preserved throughout the network. By integrating the C2f module, YOLOv8 achieves full gradient flow and more efficient learning, further facilitated by the model's architecture that minimizes the number of parameters. The number of blocks in each stage has been optimized to 3, 6, 6, and 3 blocks in Stages 1 through 4, respectively. This reduces the computational costs while maintaining high performance. Furthermore, including the Spatial Pyramid Pooling-Fast (SPPF) module in Stage 4 enhances inference speed, balancing its learning capability and efficiency [35]. Figure 2 illustrates the architecture of the YOLOv8 detection algorithm, showcasing the ranked composition of the model's three main components: the Backbone, Neck, and Head. The backbone is responsible for the initial feature extraction using the Cross-Stage Partial Architecture (CSP), which enhances the learning capabilities at low computational costs. Further, the ConvModule and C2f modules are used in various stages to facilitate effective feature propagation across different layers. The C2 module in YOLOv8 stands for the CSP (Cross-Stage Partial) Bottleneck with two convolutions. It is a building block used in the YOLOv8 architecture. The C2f module is a faster implementation of the C2 module. It improves the execution speed of the model while maintaining similar performance. This optimization is achieved by making certain modifications to the original C2 module. The neck of YOLOv8 plays a critical role in combining the multi-scale features and merging outputs from different network layers to improve prediction accuracy. Although deep networks capture more features, they sometimes need more precise location information for small objects, due to large-scale convolution operations. YOLOv8 combines Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) architectures to reduce this [24]. Drawing inspiration from YOLOv5 [36], the FPN in YOLOv8 up-samples features from higher layers to enhance the feature information in the lower layers.