# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 4 Results and Discussion
### Ablation Study and Quantitative Analysis
While the standard YOLOv8 model fluctuates in precision, the HA YOLO model offers consistent performance and reduces the swings in accuracy. Another difference between both models is that the HA model reaches 100% at a confidence threshold of 0.962, whereas the regular YOLOv8 model achieves this at 0.849. This means that the HA model is more cautious about making predictions, but it makes up for it with its reliability across lower confidence thresholds. While precision measures the accuracy of the model's predictions, recall, which is responsible for identifying the model's actual positives, is important. The Recall-Confidence Curve plots recall as a function of the confidence threshold value. As the confidence threshold value increases, the recall will typically decrease because it forces the model to become more selective and may miss accurate positive detections. In the Recall-Confidence Curve (Figure 8), recall is usually higher at low confidence thresholds because the model is not as strict and detects more possible fractures. However, if the confidence threshold rises, the model will become more confident in its predictions, but some true positives may be missed, leading to recall dropping. Balancing precision and recall is challenging, so the precision-confidence and recall-confidence curves are essential to consider together. As shown in Figure 8, with low confidence thresholds (around 0), the recall is around 0.6-0.7. This means the model is more lenient and considers more potential fractures while detecting false positives as true positives due to low confidence levels. Essentially, while the model detects more fractures, it does so at the cost of being less precise. As the confidence threshold increases, the recall steadily declines due to the model being more selective with its accurate positive detections. Between the confidence range of 0.4-0.6, recall is between 0.4-0.5, showing that the model is starting to miss more true fractures as it becomes stricter in its detections. This drop can happen because the model prioritizes precision over recall at high confidence levels, leading to fewer predictions but with higher accuracy. Figure 7: Detailed illustration of the Precision-Confidence Curve of the YOLOv8-HA model with the input image size of 640. As we see in Figure 9, the HA model starts with a high recall between 0.5 and 0.6. This shows that when the model is less selective, it can detect many actual fractures. The HA mechanism enhances the model's performance at the early stages by ensuring that a good number of actual fractures are detected, even though some might be false positives. This indicates that the attention modules are helping the model focus on relevant features early, improving recall without sacrificing accuracy. Figure 8: Detailed illustration of the Recall-Confidence Curve of the YOLOv8 model with the input image size of 640. Figure 9: Detailed illustration of the Recall-Confidence Curve of the YOLOv8-HA model with the input image size of 640. Compared to the standard YOLOv8 model, the HA model shows greater consistency at low- and mid-confidence thresholds. This makes it particularly important in applications like fracture detection, where missing actual fractures can have consequences. The consistent recall curve across the thresholds shows reliable detection without extreme trade-offs, as seen in models that fluctuate more inconsistently. The sharper drop at around 0.8 confidence level shows the expected behavior as the model focuses on precision rather than recall. However, it maintains a better balance than the standard YOLOv8 model. While recall measures the model's ability to detect positive detections, it is important to keep the Precision-Recall curve in mind, which helps measure the success of the prediction when the classes are imbalanced; the Precision-Recall Curve shows the tradeoff between precision and recall for different threshold values.