Fine-grained wound tissue analysis using deep neural network

TL;DR

This paper introduces a deep neural network approach for classifying seven distinct wound tissue types, addressing limitations of previous methods that only identified three types, thereby improving clinical wound assessment accuracy.

Contribution

The study is the first to classify all seven tissue types in chronic wounds using deep learning, supported by a new publicly available database and patch-level analysis.

Findings

Outperforms state-of-the-art methods in tissue classification accuracy

Develops a new database of wound tissue images for research

Demonstrates the effectiveness of pre-trained neural networks for clinical tissue analysis

Abstract

Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic wound tissue analysis, there has been a shortcoming in these approaches for clinical practices. In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure. In this paper, for the first time, we investigate the classification of 7 wound issue types. We work with wound professionals to build a new database of 7 types of wound tissue. We propose to use pre-trained deep neural networks for feature extraction and classification at the patch-level. We perform experiments to demonstrate that our approach outperforms other state-of-the-art. We will make our database publicly available to facilitate research in wound assessment.