  We present an automated wound localizer from 2D wound and ulcer images by
using deep neural network, as the first step towards building an automated and
complete wound diagnostic system. The wound localizer has been developed by
using YOLOv3 model, which is then turned into an iOS mobile application. The
developed localizer can detect the wound and its surrounding tissues and
isolate the localized wounded region from images, which would be very helpful
for future processing such as wound segmentation and classification due to the
removal of unnecessary regions from wound images. For Mobile App development
with video processing, a lighter version of YOLOv3 named tiny-YOLOv3 has been
used. The model is trained and tested on our own image dataset in collaboration
with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is
compared with SSD model, showing that YOLOv3 gives a mAP value of 93.9%, which
is much better than the SSD model (86.4%). The robustness and reliability of
these models are also tested on a publicly available dataset named Medetec and
shows a very good performance as well.
