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

arXiv: https://arxiv.org/abs/2303.16522 · HTML: https://mlsanity.com/paper/2303.16522/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction](https://mlsanity.com/paper/1801.08297/) (2018)
- [Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering](https://mlsanity.com/paper/1711.00108/) (2017)
- [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/1711.10448/) (2017)
- [Image Based Artificial Intelligence in Wound Assessment: A Systematic Review](https://mlsanity.com/paper/2009.07141/) (2020)
- [Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks](https://mlsanity.com/paper/1807.04355/) (2018)

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

- [Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis](https://mlsanity.com/paper/2504.10452/) (2025)
- [Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)](https://mlsanity.com/paper/2505.08086/) (2025)
- [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026)

## Abstract (arXiv)

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.