  The process of wound healing has been an active area of research around the
world. The problem is the wounds of different patients heal differently. For
example, patients with a background of diabetes may have difficulties in
healing [1]. By clearly understanding this process, we can determine the type
and quantity of medicine to give to patients with varying types of wounds. In
this research, we use a variation of the Alternating Direction Implicit method
to solve a partial differential equation that models part of the wound healing
process. Wound images are used as the dataset that we analyze. To segment the
image's wound, we implement deep learning-based models. We show that the
combination of a variant of the Alternating Direction Implicit method and Deep
Learning provides a reasonably accurate model for the process of wound healing.
To the best of our knowledge, this is the first attempt to combine both
numerical PDE and deep learning techniques in an automated system to capture
the long-term behavior of wound healing.
