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.
In this research, we used a wound image dataset collected over a two-year clinical period at the AZH Wound and Vascular Center in Milwaukee, Wisconsin. The dataset includes 400 wound images in jpg format and various sizes ranging from 240 × 320 to 525 × 700 pixels and bit depth of 24 from four different wound types: venous, diabetic, pressure, and surgical (100 images per class which generates a balanced dataset).
B. Rostami, D.M. Anisuzzaman, C. Wang, S. Gopalakrishnan, J. Niezgoda, and Z. Yu, “Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier”, Computers in Biology and Medicine, 134:104536, 2021.