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

Source: [Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier](<https://www.sota2.com/research/paper/68fa6a0c1e209131cea25bea>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:15:37.395Z

Authors: Behrouz Rostami, D.M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu

Published: 2020-10-19T15:20:12.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.09593>)

[PDF](<https://arxiv.org/pdf/2010.09593.pdf>)