In collaboration with Bioengineering, an open access journal by MDPI, we are soliciting papers in various AI technologies for wound assessment. More detail can be found below or at the journal's webpage.
The wound dataset has been collected from the AZH Wound and Vascular Center, Milwaukee, WI, USA. This dataset (AZH Wound Database) contains a total of 1,010 wound images. Three types of ulcers have been included in the dataset: Diabetic foot ulcer (DFU), Pressure Ulcer (PU), and Venous Ulcer (VU). All the images are captured with iPad and DSLR cameras. No specific environmental or illumination condition has been applied during image capturing. These images are further processed and used as training and test data.
D. M. Anisuzzaman, Y. Patel, J. A. Niezgoda, S. Gopalakrishnan and Z. Yu, "A Mobile App for Wound Localization Using Deep Learning," in IEEE Access, vol. 10, pp. 61398-61409, 2022, doi: 10.1109/ACCESS.2022.3179137.