A Nesting Segmenting and Categorizing Model for Diabetic Wound Depth Detection in Foot Using Convolutional Neural Networks

Background: Traditional methods of Diabetic Foot Ulcers (DFUs) assessment and segmentation using computer vision and Machine Learning (ML) have faced limitations in accuracy and automation. Recent advancements in Deep Learning (DL) offer potential improvements, but challenges remain in the precise quantification and categorization of DFUs. Methods: This study explores a framework combining Mask Region-based Convolutional Neural Networks (R-CNN) with a nested structure for DFU segmentation. The approach involved collecting and preprocessing DFU images from Xiangya Hospital and the DFU Challenge (DFUC) 2020. A Region Proposal Network (RPN) was employed to identify potential regions of interest, which were then analyzed using Mask R-CNN with ResNet-FPN as the backbone. The model aimed to enhance feature extraction and categorization of DFUs through a layered approach that integrates regional proposals and segmentation masks. Results: The proposed method achieved superior results in DFU categorization compared to existing techniques. The use of Mask R-CNN with ResNet-FPN improved segmentation accuracy, with the framework achieving notable enhancements in F-score and accuracy metrics—0.79%, 16.30%, and 3.24% improvements over other algorithms. The method effectively learned and categorized complex DFU structures, providing a more accurate assessment of wound severity levels. The performance was validated against various models, including R-CNN, YOLO, DetNet, and EfficientDet, demonstrating the robustness of the proposed method. Conclusion: The integration of Mask R-CNN with a nested feature extraction framework offers a promising advancement in DFU segmentation. This approach addresses existing limitations in automated DFU assessment by improving accuracy and consistency.