# ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization

arXiv: https://arxiv.org/abs/2209.12822 · HTML: https://mlsanity.com/paper/2209.12822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (2020)
- [Fine-grained wound tissue analysis using deep neural network](https://mlsanity.com/paper/1802.10426/) (2018)

## Built on by (most cited first, 3 of 3)

- [Wound3DAssist: A Practical Framework for 3D Wound Assessment](https://mlsanity.com/paper/2508.17635/) (2025)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation](https://mlsanity.com/paper/2608.25866/) (2026)

## Abstract (arXiv)

Complex wounds usually face partial or total loss of skin thickness, healing by secondary intention. They can be acute or chronic, figuring infections, ischemia and tissue necrosis, and association with systemic diseases. Research institutes around the globe report countless cases, ending up in a severe public health problem, for they involve human resources (e.g., physicians and health care professionals) and negatively impact life quality. This paper presents a new database for automatically categorizing complex wounds with five categories, i.e., non-wound area, granulation, fibrinoid tissue, and dry necrosis, hematoma. The images comprise different scenarios with complex wounds caused by pressure, vascular ulcers, diabetes, burn, and complications after surgical interventions. The dataset, called ComplexWoundDB, is unique because it figures pixel-level classifications from 27 images obtained in the wild, i.e., images are collected at the patients' homes, labeled by four health professionals. Further experiments with distinct machine learning techniques evidence the challenges in addressing the problem of computer-aided complex wound tissue categorization. The manuscript sheds light on future directions in the area, with a detailed comparison among other databased widely used in the literature.