  Diabetic foot ulcers are a common manifestation of lesions on the diabetic
foot, a syndrome acquired as a long-term complication of diabetes mellitus.
Accompanying neuropathy and vascular damage promote acquisition of pressure
injuries and tissue death due to ischaemia. Affected areas are prone to
infections, hindering the healing progress. The research at hand investigates
an approach on classification of infection and ischaemia, conducted as part of
the Diabetic Foot Ulcer Challenge (DFUC) 2021. Different models of the
EfficientNet family are utilized in ensembles. An extension strategy for the
training data is applied, involving pseudo-labeling for unlabeled images, and
extensive generation of synthetic images via pix2pixHD to cope with severe
class imbalances. The resulting extended training dataset features $8.68$ times
the size of the baseline and shows a real to synthetic image ratio of $1:3$.
Performances of models and ensembles trained on the baseline and extended
training dataset are compared. Synthetic images featured a broad qualitative
variety. Results show that models trained on the extended training dataset as
well as their ensemble benefit from the large extension. F1-Scores for rare
classes receive outstanding boosts, while those for common classes are either
not harmed or boosted moderately. A critical discussion concretizes benefits
and identifies limitations, suggesting improvements. The work concludes that
classification performance of individual models as well as that of ensembles
can be boosted utilizing synthetic images. Especially performance for rare
classes benefits notably.
