  Tissue assessment for chronic wounds is the basis of wound grading and
selection of treatment approaches. While several image processing approaches
have been proposed for automatic wound tissue analysis, there has been a
shortcoming in these approaches for clinical practices. In particular,
seemingly, all previous approaches have assumed only 3 tissue types in the
chronic wounds, while these wounds commonly exhibit 7 distinct tissue types
that presence of each one changes the treatment procedure. In this paper, for
the first time, we investigate the classification of 7 wound issue types. We
work with wound professionals to build a new database of 7 types of wound
tissue. We propose to use pre-trained deep neural networks for feature
extraction and classification at the patch-level. We perform experiments to
demonstrate that our approach outperforms other state-of-the-art. We will make
our database publicly available to facilitate research in wound assessment.
