Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks
This work addresses wound monitoring for medical specialists, but it is incremental as it applies an existing method to a specific dataset with modest gains.
The study tackled the problem of classifying burn wound images into categories for diagnosis by fine-tuning a pre-trained AlexNet model, achieving an 8% improvement in classification accuracy compared to previous works on the same dataset.
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%.