  The classification of wound severity is a critical step in wound diagnosis.
An effective classifier can help wound professionals categorize wound
conditions more quickly and affordably, allowing them to choose the best
treatment option. This study used wound photos to construct a deep neural
network-based wound severity classifier that classified them into one of three
classes: green, yellow, or red. The green class denotes wounds still in the
early stages of healing and are most likely to recover with adequate care.
Wounds in the yellow category require more attention and treatment than those
in the green category. Finally, the red class denotes the most severe wounds
that require prompt attention and treatment. A dataset containing different
types of wound images is designed with the help of wound specialists. Nine deep
learning models are used with applying the concept of transfer learning.
Several stacked models are also developed by concatenating these transfer
learning models. The maximum accuracy achieved on multi-class classification is
68.49%. In addition, we achieved 78.79%, 81.40%, and 77.57% accuracies on green
vs. yellow, green vs. red, and yellow vs. red classifications for binary
classifications.
