# Fine-grained wound tissue analysis using deep neural network

arXiv: https://arxiv.org/abs/1802.10426 · HTML: https://mlsanity.com/paper/1802.10426/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization](https://mlsanity.com/paper/2209.12822/) (2022)
- [Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset](https://mlsanity.com/paper/2502.10652/) (2025)
- [Wound Severity Classification using Deep Neural Network](https://mlsanity.com/paper/2204.07942/) (2022)
- [Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers](https://mlsanity.com/paper/2405.00858/) (2024)

## Abstract (arXiv)

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.