Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models

1. 1Develop a deep learning model based on YOLOv11
2. 2Train the model to simultaneously perform wound boundary segmentation and multi-class classification
3. 3Evaluate the model's performance on a dataset of wound images
4. 4Fine-tune the model to improve its accuracy and clinical applicability

Medical professionals and AI researchers can benefit from this study as it enhances the accuracy of wound classification and boundary segmentation, leading to better clinical decisions

💡 Joint boundary segmentation and multi-class classification can improve the accuracy of automated wound assessment

💡 AI-powered wound assessment just got a boost! New deep learning model jointly segments boundaries & classifies wounds