# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 4 Results and Discussion
### Ablation Study and Quantitative Analysis  
To showcase the effectiveness of the HA mechanism, we performed an ablation study focusing mainly on the YOLOv8m variant. The results revealed that the m-model excelled in detecting hand fractures, achieving mAP scores over 0.7 for that class. Table 2 illustrates the predictions for each class using our HA method, showing that the mAP for hand fractures improved from 0.59 to 0.71, giving prominence to the benefits of our modifications. Additionally, we combined the arms and leg classes, resulting in our HA model achieving a precision score of 0.592, outperforming the standard YOLOv8 model, which only had a precision of 0.476. These results show the performance capabilities of our HA mechanism for enhancing detection accuracy. Additionally, we observed a significant improvement in training speed with the YOLOv8-HA mechanism. Training the original YOLOv8m model using the SGD optimizer required 6791 s and 4979 s with the Adam optimizer. In comparison, the YOLOv8m-HybridAttention model completed the training in 5931 s with SGD and 3708 s with Adam optimizer. These results show that the HA mechanism improves accuracy and reduces training time, highlighting its efficiency in enhancing model performance without taking up more computational resources. Older versions of YOLO models were tested for comparison and showed lower precision accuracy. Figure 5: Examples of fracture detection on X-ray images from the FracAtlas dataset. (**a**) Manually labeled images, (**b**) Predicted images (indicated by 0). Further quantitative analysis was performed. The precision-confidence curve in Figure 6 shows the model's performance in detecting fractures. The curve plots the ratio of accurate positive predictions to all positive predictions as the confidence threshold changes. The model uses the confidence threshold to determine its certainty before labeling a detection as positive. We can adjust the threshold, and the model can become more lenient or stricter with its detections. The precision between 0.0 and 0.4 is relatively low, indicating that the model makes many predictions, some of which are false positives. This can happen when the model is not confident enough, so it considers many potential fractures as a valid detection. As a result, more incorrect positive detections are mixed with correct detections. As the confidence threshold increases, the precision rises. Between the range of 0.6-0.8, the model's accuracy is above 80%, indicating that the model is doing a much better job detecting actual fractures and reducing false positives. This could be the model's sweet spot, balancing precision and recall, ensuring that most predictions are correct. Figure 7 illustrates several improvements compared to the regular YOLOv8 model. First, it starts with a higher precision at lower confidence thresholds, around 0.6. This can mean that the attention mechanisms, spatial and channel, help improve the model's ability to identify a fracture early on, even if the model is not as confident. This boost is essential in reducing the false positive right from the start. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** & **Time** **(Seconds)** \\ \hline Yolov8m (Arm) & 0.594 & 0.491 & 0.538 & 6791 \\ YOLOv8m-HA(Arm) & 0.713 & 0.642 & 0.676 & 5931 \\ YOLOv8m (Arm and Leg) & 0.476 & 0.418 & 0.445 & 1763 \\ YOLOv8m-HA (Arm and Leg) & 0.592 & 0.473 & 0.526 & 1698 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance comparison of YOLOv8m to our YOLOv8-HA model. Figure 6: Detailed illustration of the Precision-Confidence Curve of the YOLOv8 model with the input image size of 640. Furthermore, the HA model can maintain a more stable precision across a range of confidence thresholds. From 0.4-0.8, the model consistently holds its precision above 0.7 and 0.8. The stable precision indicates that the attention modules improve reliability across different confidence levels.