Document Type

Article

Publication Date

2021

DOI

10.3390/electronics10121406

Publication Title

Electronics

Volume

10

Issue

12

Pages

1-15

Abstract

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

Comments

© 2021 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Original Publication Citation

Sarp, S., Kuzlu, M., Wilson, E., Cali, U., & Guler, O. (2021). The enlightening role of explainable artificial intelligence in chronic wound classification. Electronics, 10(12), 1-15, Article 1406. https://doi.org/10.3390/electronics10121406

ORCID

0000-0002-8719-2353 (Kuzlu)

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