  Millions of people are affected by acute and chronic wounds yearly across the
world. Continuous wound monitoring is important for wound specialists to allow
more accurate diagnosis and optimization of management protocols. Machine
Learning-based classification approaches provide optimal care strategies
resulting in more reliable outcomes, cost savings, healing time reduction, and
improved patient satisfaction. In this study, we use a deep learning-based
method to classify burn wound images into two or three different categories
based on the wound conditions. A pre-trained deep convolutional neural network,
AlexNet, is fine-tuned using a burn wound image dataset and utilized as the
classifier. The classifier's performance is evaluated using classification
metrics such as accuracy, precision, and recall as well as confusion matrix. A
comparison with previous works that used the same dataset showed that our
designed classifier improved the classification accuracy by more than 8%.
