  Increasing numbers of patients with disabilities or elderly people with
mobility issues often suffer from a pressure ulcer. The affected areas need
regular checks, but they have a difficulty in accessing a hospital. Some remote
diagnosis systems are being used for them, but there are limitations in
checking a patient's status regularly. In this paper, we present a remote
medical assistant that can help pressure ulcer management with image processing
techniques. The proposed system includes a mobile application with a deep
learning model for wound segmentation and analysis. As there are not enough
data to train the deep learning model, we make use of a pretrained model from a
relevant domain and data augmentation that is appropriate for this task. First
of all, an image preprocessing method using bilinear interpolation is used to
resize images and normalize the images. Second, for data augmentation, we use
rotation, reflection, and a watershed algorithm. Third, we use a pretrained
deep learning model generated from skin wound images similar to pressure ulcer
images. Finally, we added an attention module that can provide hints on the
pressure ulcer image features. The resulting model provides an accuracy of
99.0%, an intersection over union (IoU) of 99.99%, and a dice similarity
coefficient (DSC) of 93.4% for pressure ulcer segmentation, which is better
than existing results.
