[An Improved Faster R-CNN Method for Wound Detection].

Objective: By introducing an attention enhancement mechanism and improving the post-processing strategy, the Faster Region-based Convolutional Neural Network (Faster R-CNN) is enhanced. The resulting improved Faster R-CNN method for wound detection (WD-IFRCNN) increases the accuracy and stability of wound detection. Methods: ① Algorithm construction: The 50-layer residual network (ResNet-50) is used as the backbone. In the fourth residual stage (convolutional stage 4, Conv4_x) and fifth residual stage (convolutional stage 5, Conv5_x), the fuzzy mask attention module (FMAM) and convolutional block attention module (CBAM) are embedded together to enhance the model's ability to represent features in key areas and blurry regions of the wound surface. At the same time, soft non-maximum suppression (Soft-NMS) replaces the traditional non-maximum suppression strategy to reduce missed detections in overlapping target scenarios. ② Algorithm validation: The experimental dataset consists of open-source wound images and clinically collected images, totaling 740 original images. After data augmentation, the dataset expands to 5920 images and is evaluated using ten-fold cross-validation. Model performance is assessed through internal validation, external validation, hyperparameter tuning, and ablation experiments, using metrics such as precision, recall, average precision, and F1 score. Results: ① Internal validation showed that the model performs best when CBAM is embedded in both the Conv4_x and Conv5_x stages of ResNet-50. When ResNet-50-CBAM is used as the backbone, the model's detection performance surpasses that of VGG16 and ResNet-50. ② External validation showed that WD-IFRCNN achieves an precision of 92.31%, recall of 93.95%, average precision of 92.33%, and F1 score of 0.93. The average precision was 3.21%, 2.30%, 1.39%, 0.86%, 0.82%, 0.37%, and 0.63% higher than SSD, YOLOv4, YOLOv5, YOLOv8, DETR, RT-DETR, and FR-CNN-FPN, respectively. ③ Ablation experiments showed that CBAM, FMAM, and Soft-NMS each positively impact model performance, with the best results achieved when all are used together. Conclusion: WD-IFRCNN effectively enhances the model's ability to represent features in key areas and blurry regions of the wound surface, improves the accuracy and stability of wound detection, and demonstrates good adaptability to complex wound scenarios. It can provide technical support for clinical wound assessment and auxiliary diagnosis.