  Identification of burn depth with sufficient accuracy is a challenging
problem. This paper presents a deep convolutional neural network to classify
burn depth based on altered tissue morphology of burned skin manifested as
texture patterns in the ultrasound images. The network first learns a
low-dimensional manifold of the unburned skin images using an encoder-decoder
architecture that reconstructs it from ultrasound images of burned skin. The
encoder is then re-trained to classify burn depths. The encoder-decoder network
is trained using a dataset comprised of B-mode ultrasound images of unburned
and burned ex vivo porcine skin samples. The classifier is developed using
B-mode images of burned in situ skin samples obtained from freshly euthanized
postmortem pigs. The performance metrics obtained from 20-fold cross-validation
show that the model can identify deep-partial thickness burns, which is the
most difficult to diagnose clinically, with 99% accuracy, 98% sensitivity, and
100% specificity. The diagnostic accuracy of the classifier is further
illustrated by the high area under the curve values of 0.99 and 0.95,
respectively, for the receiver operating characteristic and precision-recall
curves. A post hoc explanation indicates that the classifier activates the
discriminative textural features in the B-mode images for burn classification.
The proposed model has the potential for clinical utility in assisting the
clinical assessment of burn depths using a widely available clinical imaging
device.
