Version 1
: Received: 17 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (14:28:04 CET)
How to cite:
Sarp, S.; Kuzlu, M.; Wilson, E.; Cali, U.; Guler, O. A Highly Transparent and Explainable Artificial Intelligence Tool for Chronic Wound Classification: XAI-CWC. Preprints2021, 2021010346. https://doi.org/10.20944/preprints202101.0346.v1
Sarp, S.; Kuzlu, M.; Wilson, E.; Cali, U.; Guler, O. A Highly Transparent and Explainable Artificial Intelligence Tool for Chronic Wound Classification: XAI-CWC. Preprints 2021, 2021010346. https://doi.org/10.20944/preprints202101.0346.v1
Sarp, S.; Kuzlu, M.; Wilson, E.; Cali, U.; Guler, O. A Highly Transparent and Explainable Artificial Intelligence Tool for Chronic Wound Classification: XAI-CWC. Preprints2021, 2021010346. https://doi.org/10.20944/preprints202101.0346.v1
APA Style
Sarp, S., Kuzlu, M., Wilson, E., Cali, U., & Guler, O. (2021). A Highly Transparent and Explainable Artificial Intelligence Tool for Chronic Wound Classification: XAI-CWC. Preprints. https://doi.org/10.20944/preprints202101.0346.v1
Chicago/Turabian Style
Sarp, S., Umit Cali and Ozgur Guler. 2021 "A Highly Transparent and Explainable Artificial Intelligence Tool for Chronic Wound Classification: XAI-CWC" Preprints. https://doi.org/10.20944/preprints202101.0346.v1
Abstract
Artificial Intelligence (AI) has seen increased application and widespread adoption over the past decade despite, at times, offering a limited understanding of its inner working. AI algorithms are, in large part, built on weights, and these weights are calculated as a result of large matrix multiplications. Computationally intensive processes are typically harder to interpret. Explainable Artificial Intelligence (XAI) aims to solve this black box approach 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. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.
Keywords
Chronic wound classification; transfer learning; explainable artificial intelligence.
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
