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Increasing numbers of patients with disabilities or elderly people withmobility issues often suffer from a pressure ulcer. The affected areas needregular checks, but they have a difficulty in accessing a hospital. Some remotediagnosis systems are being used for them, but there are limitations inchecking a patient's status regularly. In this paper, we present a remotemedical assistant that can help pressure ulcer management with image processingtechniques. The proposed system includes a mobile application with a deeplearning model for wound segmentation and analysis. As there are not enoughdata to train the deep learning model, we make use of a pretrained model from arelevant domain and data augmentation that is appropriate for this task. Firstof all, an image preprocessing method using bilinear interpolation is used toresize images and normalize the images. Second, for data augmentation, we userotation, reflection, and a watershed algorithm. Third, we use a pretraineddeep learning model generated from skin wound images similar to pressure ulcerimages. Finally, we added an attention module that can provide hints on thepressure ulcer image features. The resulting model provides an accuracy of99.0%, an intersection over union (IoU) of 99.99%, and a dice similaritycoefficient (DSC) of 93.4% for pressure ulcer segmentation, which is betterthan existing results.

A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation