# Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier

arXiv: https://arxiv.org/abs/2010.09593 · HTML: https://mlsanity.com/paper/2010.09593/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network](https://mlsanity.com/paper/2109.06969/) (2021)
- [Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach](https://mlsanity.com/paper/2308.11877/) (2023)
- [Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2111.06894/) (2021)
- [Wound Severity Classification using Deep Neural Network](https://mlsanity.com/paper/2204.07942/) (2022)
- [Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections](https://mlsanity.com/paper/2202.13496/) (2022)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)
- [A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning](https://mlsanity.com/paper/2411.02456/) (2024)
- [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)

## Abstract (arXiv)

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists the specialists in the field to classify the wounds with less financial and time costs. Different machine learning and deep learning-based wound classification methods have been proposed in the literature. In this study, we have developed an ensemble Deep Convolutional Neural Network-based classifier to classify wound images including surgical, diabetic, and venous ulcers, into multi-classes. The output classification scores of two classifiers (patch-wise and image-wise) are fed into a Multi-Layer Perceptron to provide a superior classification performance. A 5-fold cross-validation approach is used to evaluate the proposed method. We obtained maximum and average classification accuracy values of 96.4% and 94.28% for binary and 91.9% and 87.7% for 3-class classification problems. The results show that our proposed method can be used effectively as a decision support system in classification of wound images or other related clinical applications.