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

• URL: http://arxiv.org/abs/2010.09593v1
• Date: Mon, 19 Oct 2020 15:20:12 GMT
• Title: Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier
• Authors: Behrouz Rostami, D.M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu
• Abstract summary: We have developed an ensemble Deep Convolutional Neural Network-based classifier to classify wound images into multi-classes. 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.
• Score: 2.07811670193148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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