# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 4 Results and Discussion
### Ablation Study and Quantitative Analysis
As shown in Figure 10, the Precision-Recall curve of the regular YOLOv8 model shows how the performance shifts as it makes more predictions or if it focuses on making accurate predictions. The model starts with high precision when the recall is low, which suggests that most predictions are correct. However, as the recall value increases, the precision starts to drop. The drop means that more false positives are being detected, impacting accuracy. Figure 11 illustrates the HA YOLOv8 model's Precision-Recall Curve, demonstrating the improved balance between precision and recall. The HA mechanism allows the model to maintain higher precision even as the recall increases, as evidenced by the less steep drop in precision as the recall value increases. The Hybrid model can keep the precision higher and steadier from about 0.6 until recall reaches about 0.4, proving that it performs better at detecting fractures while maintaining high accuracy. Even as recall increases, the precision decline is more gradual than the regular YOLOv8 model. This improved balance from the HA mechanism suggests it is more effective. We compared the modified algorithm introduced in this study to other state-of-the-art object detection models for fracture detection, including popular frameworks and specialized models. A modified YOLOv2 achieved a mAP of 75.3% on a spinal fracture lesion dataset, excelling in time-sensitive environments with a detection speed of 0.027 s per image. Similarly, YOLOv8, trained on augmented fracture scans from the GRAZPEDWRI-DX dataset, achieved the same mAP. YOLOv7, enhanced with a Squeeze-and-Excitation  
Figure 10: Detailed illustration of the Precision-Recall Curve of our YOLOv8 model with the input image size of 640.  
module, reached a mAP of 86.2% on the FracAtlas dataset. Meanwhile, YOLO-NAS combined with EfficientDet delivered the best result, achieving a remarkable 98.1% mAP on a hand fracture dataset. Our HA YOLOv8 model achieved an accuracy of 0.713 on hand fractures, which, although lower than some models, offers several key advantages. While our model does not have the highest accuracy, its balance of performance, improved speed, interpretability, and resource efficiency makes it well-suited for clinical applications, where speed and detailed analysis are critical, even when image quality could be better. Our results were obtained from a dataset with unique challenges, such as complex fracture patterns, imaging conditions, and fewer training images than other datasets. Comparing models across different datasets with varying complexities is very complex. More accurate models may have been trained on specialized datasets optimized for specific fracture types or custom hand-made datasets. By integrating the HA mechanism, we improved the accuracy from 0.594 to 0.713, enhancing the model's ability to detect subtle or ambiguous fracture features. We encountered several challenges or limitations during the testing and training process before we finalized the YOLOv8 algorithm to integrate our HA mechanism. The main challenge was addressing the tradeoff between speed and accuracy while integrating the HA mechanism and testing several YOLO models. Another challenge or limitation we faced included the unavailability of reliable, publicly available annotated datasets that are less noisy for real-time image processing without consuming enormous computational resources. In the future, we plan to integrate our proposed model into emerging architectures, such as YOLOv10, and capsule network-based algorithms, such as CapsNets [43] or TSPORTNet [44], which simplifies the overall architecture, using less energy and resources while improving accuracy and precision. Various device applications will also be tested for adaptability to different hardware. We plan to expand to other domains and not limit ourselves to medical imaging. Figure 11: Detailed illustration of the Precision-Recall Curve of the YOLOv8-HA model with the input image size of 640.