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

TL;DR

This paper presents an ensemble deep CNN classifier for multi-class wound image classification, achieving high accuracy and aiding clinical decision-making with reduced costs.

Contribution

It introduces a novel ensemble deep learning approach combining patch-wise and image-wise classifiers with MLP for improved wound classification accuracy.

Findings

Maximum accuracy of 96.4% for binary classification

Average accuracy of 87.7% for 3-class classification

Effective as a decision support system in clinical settings

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.