改进YOLOv8检测儿童手腕骨折，准确率提升至65.8%
YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection
- 在YOLOv8中加入带残差结构的注意力模块
- 在GRAZPEDWRI-DX数据集上mAP50达65.8%
- 适合医疗影像检测与模型优化研究者
手腕创伤和骨折在日常生活中常见，尤其儿童占比高。术前常通过X光片进行诊断。随着神经网络发展，YOLO系列模型广泛应用于计算机辅助诊断中的骨折检测，其中YOLOv8已取得良好效果。将注意力模块引入神经网络是提升性能的有效方法。本文提出YOLOv8-ResCBAM，将融合残差结构的卷积块注意力模块（ResCBAM）嵌入原始YOLOv8架构。在GRAZPEDWRI-DX数据集上的实验表明，该模型的mAP50从原YOLOv8的63.6%提升至65.8%，达到当前最优水平。代码已开源：https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8。
原文摘要 · Abstract (English)
Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first, and prepare for the surgery based on the analysis of the X-ray images. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection for Computer-Assisted Diagnosis, where the YOLOv8 model has obtained the satisfactory results. Applying the attention modules to neural networks is one of the effective methods to improve the model performance. This paper proposes YOLOv8-ResCBAM, which incorporates Convolutional Block Attention Module integrated with resblock (ResCBAM) into the original YOLOv8 network architecture. The experimental results on the GRAZPEDWRI-DX dataset demonstrate that the mean Average Precision calculated at Intersection over Union threshold of 0.5 (mAP 50) of the proposed model increased from 63.6% of the original YOLOv8 model to 65.8%, which achieves the state-of-the-art performance. The implementation code is available at https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.
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