# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 2 Materials and Methods
### AI Network
#### 2.3.2 Proposed Network: CTA-YOLOv8  
The proposed CTA-YOLOv8 network architecture is illustrated in Figure 7. Each network underwent five training iterations using the parameters that yielded the highest accuracy. Specifically, the batch size, epochs, learning rate, and weight decay were set to 32, 300, 0.001, and 0.005. Training was halted prematurely if there was no improvement in performance over 10 epochs based on the validation data. Additionally, a hyperparameter evolution technique was employed during each training session to optimize the remaining hyperparameters.  
We designed our CTA-YOLOv8 by integrating an attention block, comprising the CBAM and the Swin Transformer block. CBAM was recognized for its efficacy in enhancing  
Figure 6: YOLOv8 network: (**a**) convolution block; (**b**) C2f block; (**c**) Spatial Pyramid Pooling Fast (SPFF) block; (**d**) detect block.  
Figure 7: (**a**) YOLOv8; (**b**) CT Attention-YOLOv8 (CTA-YOLOv8).  
the accuracy of object detection networks and is commonly utilized in YOLOv8 object detection networks. The structure of CBAM is illustrated in Figure 8[20]. In YOLOv8, The CBAM was integrated into the network by utilizing code already embedded within the YOLOv8 network and was embedded to replace the C2F position. In this study, we prioritized resource efficiency given the challenges associated with acquiring workstations with substantial resources for clinical use. This consideration is in line with our primary goal of clinical application. Thus, we aim to minimize resource overhead by applying the CBAM module while maintaining the basic structure of YOLOv8, specifically the C2f layer. Additionally, we sought to identify the optimal position for the CBAM module that aligns with our dataset. The ideal position for the CBAM was found to be at the end C2f part of the backbone.  
The Swin Transformer block is known to effectively enhance the accuracy of object detection networks and is commonly used in YOLOv8 object detection networks [21]. The structure is shown in Figure 9.  
Especially in the context of YOLOv8, which was based on CNN for object detection, the integration of a transformer-based attention block like the Swin Transformer block was anticipated to enhance accuracy by leveraging global information. This was particularly pertinent in CT data analysis, where the clear differentiation of objects from their surroundings was crucial for medical image interpretation.  
Hence, QC evaluations also emphasized this aspect, recognizing the significance of the relationship between objects and their surroundings. To implement the Swin Transformer block into YOLOv8, we performed the development of a novel module through specialized structural adjustments. Accordingly, we devised the Swin Transformer block like Figure 10 to substitute the circuitry structure of the bottleneck in C2f. Instead of introducing an additional feature extraction process, we replaced the position of the C2f module with the attention block to enhance network performance without compromising computational efficiency. The ideal positioning of attention blocks was determined during training to  
Figure 8: CBAM block.  
Figure 9: Swin Transformer: (**a**) patch partition; (**b**) linear embedding; (**c**) layer normalization; (**d**) Window Multi-head Self-Attention (W-MSA); (**e**) Shift Window Multi-head Self-Attention (SW-MSA).  
maximize accuracy. The most effective location was identified as the first C2f position, where initial feature extraction occurs, with a patch size of 6.  
In this study, we took a distinctive approach to accommodate the involvement of clinical experts by restructuring the C2f architecture and fine-tuning pixel sizes, instead of integrating the Swin Transformer attention block. This choice was made to enhance both efficiency and accuracy without expanding the model's capacity, considering the limited availability of high-capacity workstations in clinical environments.