  The aging population poses numerous challenges to healthcare, including the
increase in chronic wounds in the elderly. The current approach to wound
assessment by therapists based on photographic documentation is subjective,
highlighting the need for computer-aided wound recognition from smartphone
photos. This offers objective and convenient therapy monitoring, while being
accessible to patients from their home at any time. However, despite research
in mobile image segmentation, there is a lack of focus on mobile wound
segmentation. To address this gap, we conduct initial research on three
lightweight architectures to investigate their suitability for smartphone-based
wound segmentation. Using public datasets and UNet as a baseline, our results
are promising, with both ENet and TopFormer, as well as the larger UNeXt
variant, showing comparable performance to UNet. Furthermore, we deploy the
models into a smartphone app for visual assessment of live segmentation, where
results demonstrate the effectiveness of TopFormer in distinguishing wounds
from wound-coloured objects. While our study highlights the potential of
transformer models for mobile wound segmentation, future work should aim to
further improve the mask contours.
