The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/12/1406 |
| _version_ | 1797530512468738048 |
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| author | Salih Sarp Murat Kuzlu Emmanuel Wilson Umit Cali Ozgur Guler |
| author_facet | Salih Sarp Murat Kuzlu Emmanuel Wilson Umit Cali Ozgur Guler |
| author_sort | Salih Sarp |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:30:02Z |
| format | Article |
| id | doaj.art-23cddb437d164b52b98e7f86f302f3fd |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:30:02Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-23cddb437d164b52b98e7f86f302f3fd2023-11-21T23:41:09ZengMDPI AGElectronics2079-92922021-06-011012140610.3390/electronics10121406The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound ClassificationSalih Sarp0Murat Kuzlu1Emmanuel Wilson2Umit Cali3Ozgur Guler4Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USABatten College of Engineering & Technology, Old Dominion University, Norfolk, VA 23529, USAeKare Inc., Fairfax, VA 22031, USADepartment of Electric Power Engineering, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, NO-7491 Trondheim, NorwayeKare Inc., Fairfax, VA 22031, USAArtificial 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.https://www.mdpi.com/2079-9292/10/12/1406chronic wound classificationtransfer learningexplainable artificial intelligence |
| spellingShingle | Salih Sarp Murat Kuzlu Emmanuel Wilson Umit Cali Ozgur Guler The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification Electronics chronic wound classification transfer learning explainable artificial intelligence |
| title | The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification |
| title_full | The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification |
| title_fullStr | The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification |
| title_full_unstemmed | The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification |
| title_short | The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification |
| title_sort | enlightening role of explainable artificial intelligence in chronic wound classification |
| topic | chronic wound classification transfer learning explainable artificial intelligence |
| url | https://www.mdpi.com/2079-9292/10/12/1406 |
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