Deep transfer learning-based visual classification of pressure injuries stages

Abstract

Pressure injury follow-up and treatment is a very costly and significant health care problem for many countries. Early and accurate diagnosis and treatment planning are critical for effective treatment of pressure injuries. Interventional information retrieval methods are both painful for patients and increase the risk of infection. However, thanks to non-invasive techniques such as imaging systems, it is possible to monitor pressure wounds more easily without causing any harm to patients. The purpose of this research is to develop a deep learning-based system for the analysis and monitoring of pressure injuries that provides an automatic classification of pressure injury stages. This paper introduces the pressure injury images dataset (PIID): a novel dataset for the classification of pressure injuries stages. We hope that PIID will encourage further research on the automatic visual classification of pressure injury stages. We also perform extensive analyses on PIID using state-the-of-art convolutional neural networks architectures with the power of transfer learning and image augmentation techniques.