Fine-grained wound tissue analysis using deep neural network
This work addresses a domain-specific problem for wound care professionals by enabling more accurate tissue classification that directly impacts treatment decisions, though it is incremental as it applies existing deep learning methods to a new dataset.
This paper tackles the problem of automatic wound tissue analysis by addressing the limitation of previous approaches that only classified 3 tissue types, proposing a method to classify 7 distinct tissue types which is clinically relevant. The result is a new database and approach that outperforms state-of-the-art methods, with the database being made publicly available.
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.