Article 2022 · 21 citations

Deep transfer learning-based visual classification of pressure injuries stages

Ay, Betul, Tasar, Beyda, Utlu, Zeynep, Ay, Kevser, Aydin, Galip

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.

Keywords

Transfer learning Deep learning Pressure sore Pressure injures Pressure ulcer CNN Classification of pressure injuries stages RISK-FACTORS SEGMENTATION ULCERS

WoS Categories

Computer Science, Artificial Intelligence

Sustainable Development Goals

Good Health and Well-being

Citation

Journal: NEURAL COMPUTING & APPLICATIONS
Year: 2022
Volume: 34
Issue: 18
Pages: 16157-16168
ISSN: 0941-0643
eISSN: 1433-3058
DOI: 10.1007/s00521-022-07274-6
WoS UID: WOS:000790648300003

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