Optimizing Burn Wound Segmentation with Advanced Boundary Detection via Deep Learning and Multi-Scale Techniques

Edge detection in burn area segmentation poses significant challenges due to the irregular and highly variable boundaries of burn regions across different images. To tackle this issue, we introduce an advanced edge detection framework that harnesses deeper convolutional features for enhanced accuracy and robustness in delineating burn image boundaries. Our approach employs a convolutional neural network (CNN) architecture that integrates multiscale and multilevel feature representations, effectively capturing the diverse structures present in burn areas. This innovative method improves upon traditional CNN designs by utilizing richer feature hierarchies, enabling the model to adaptively learn intricate patterns across varying image scales and aspect ratios. Optimized through end to end training, our framework achieves superior boundary detection without requiring manual intervention. We assess the performance of our method using multiple burn image datasets, showcasing its ability to accurately identify burn contours while ensuring high computational efficiency. Furthermore, we investigate the generalizability of our technique, highlighting its potential for broader applications in medical image segmentation tasks.