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.