Development of a deep learning-based tool to assist wound classification

Po Hsuan Huang, Yi Hsiang Pan, Ying Sheng Luo, Yi Fan Chen, Yu Cheng Lo, Trista Pei Chun Chen, Cherng Kang Perng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressure wound, given color images captured using readily available cameras. The accuracy of the classification is vital for appropriate wound management. The proposed wound classification method adopts a multi-task deep learning framework that leverages the relationships among the five key wound conditions for a unified wound classification architecture. With differences in Cohen's kappa coefficients as the metrics to compare our proposed model with humans, the performance of our model was better or non-inferior to those of all human medical personnel. Our convolutional neural network-based model is the first to classify five tasks of deep, infected, arterial, venous, and pressure wounds simultaneously with good accuracy. The proposed model is compact and matches or exceeds the performance of human doctors and nurses. Medical personnel who do not specialize in wound care can potentially benefit from an app equipped with the proposed deep learning model.

Original languageEnglish
Pages (from-to)89-97
Number of pages9
JournalJournal of Plastic, Reconstructive and Aesthetic Surgery
Volume79
DOIs
StatePublished - Apr 2023

Keywords

  • Deep learning
  • Image classification
  • Wound infection
  • Wounds and injuries

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