├── README.md └── dataset ├── ROI.zip └── original images.zip /README.md: -------------------------------------------------------------------------------- 1 | # wound_classification 2 | 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). 3 | 4 | # Publication 5 | 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. 6 | -------------------------------------------------------------------------------- /dataset/ROI.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uwm-bigdata/wound_classification/6159e8327c4dcb2c2c1b3a166d374c1fb3f78307/dataset/ROI.zip -------------------------------------------------------------------------------- /dataset/original images.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/uwm-bigdata/wound_classification/6159e8327c4dcb2c2c1b3a166d374c1fb3f78307/dataset/original images.zip --------------------------------------------------------------------------------