@article{Niri:15453,
      recid = {15453},
      author = {Niri, Rania and Zahia, Sofia and Stefanelli, Alessio and  Sharma, Kaushal and Probst, Sebastian and Pichon, Swann and  Chanel, Guillaume},
      title = {Wound segmentation with U-Net using a dual attention  mechanism and transfer learning},
      publisher = {Springer},
      journal = {Journal of imaging informatics in medicine},
      address = {États-Unis. 2025-01},
      number = {ARTICLE},
      pages = {15 p.},
      abstract = {Accurate wound segmentation is crucial for the precise  diagnosis and treatment of various skin conditions through  image analysis. In this paper, we introduce a novel dual  attention U-Net model designed for precise wound  segmentation. Our proposed architecture integrates two  widely used deep learning models, VGG16 and U-Net,  incorporating dual attention mechanisms to focus on  relevant regions within the wound area. Initially trained  on diabetic foot ulcer images, we fine-tuned the model to  acute and chronic wound images and conducted a  comprehensive comparison with other state-of-the-art  models. The results highlight the superior performance of  our proposed dual attention model, achieving a Dice  coefficient and IoU of 94.1% and 89.3%, respectively, on  the test set. This underscores the robustness of our method  and its capacity to generalize effectively to new data.},
      url = {http://arodes.hes-so.ch/record/15453},
      doi = {https://doi.org/10.1007/s10278-025-01386-w},
}