# Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network

arXiv: https://arxiv.org/abs/2109.06969 · HTML: https://mlsanity.com/paper/2109.06969/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques](https://mlsanity.com/paper/1908.05317/) (2019)
- [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/1711.10448/) (2017)
- [Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier](https://mlsanity.com/paper/2010.09593/) (2020)
- [Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks](https://mlsanity.com/paper/1807.04355/) (2018)
- [A Deep Learning Study on Osteosarcoma Detection from Histological Images](https://mlsanity.com/paper/2011.01177/) (2020)
- [A Mobile App for Wound Localization using Deep Learning](https://mlsanity.com/paper/2009.07133/) (2020)

## Built on by (most cited first, 10 of 10)

- [Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach](https://mlsanity.com/paper/2308.11877/) (2023)
- [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)
- [Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections](https://mlsanity.com/paper/2202.13496/) (2022)
- [A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning](https://mlsanity.com/paper/2411.02456/) (2024)
- [Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation](https://mlsanity.com/paper/2503.05214/) (2025)
- [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026)
- [Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring](https://mlsanity.com/paper/2607.05625/) (2026)
- [In-Context Learning for Wound Classification with Small Multimodal Language Models](https://mlsanity.com/paper/2607.18819/) (2026)
- [BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation](https://mlsanity.com/paper/2609.11463/) (2026)

## Abstract (arXiv)

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map is also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information are designed with the help of wound specialists. The multi-modal network is developed by concatenating the image-based and location-based classifier's outputs with some other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 77.33% to 100% on different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95% to 98.08% on different experiments. The proposed multi-modal network also shows a significant improvement in results from the previous works of literature.