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Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data
Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453276/ https://www.ncbi.nlm.nih.gov/pubmed/37627896 http://dx.doi.org/10.3390/diagnostics13162637 |
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author | Khosa, Ikramullah Raza, Awais Anjum, Mohd Ahmad, Waseem Shahab, Sana |
author_facet | Khosa, Ikramullah Raza, Awais Anjum, Mohd Ahmad, Waseem Shahab, Sana |
author_sort | Khosa, Ikramullah |
collection | PubMed |
description | Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image–patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image–patch thermograms. |
format | Online Article Text |
id | pubmed-10453276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104532762023-08-26 Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data Khosa, Ikramullah Raza, Awais Anjum, Mohd Ahmad, Waseem Shahab, Sana Diagnostics (Basel) Article Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image–patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image–patch thermograms. MDPI 2023-08-10 /pmc/articles/PMC10453276/ /pubmed/37627896 http://dx.doi.org/10.3390/diagnostics13162637 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khosa, Ikramullah Raza, Awais Anjum, Mohd Ahmad, Waseem Shahab, Sana Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title | Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title_full | Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title_fullStr | Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title_full_unstemmed | Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title_short | Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data |
title_sort | automatic diabetic foot ulcer recognition using multi-level thermographic image data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453276/ https://www.ncbi.nlm.nih.gov/pubmed/37627896 http://dx.doi.org/10.3390/diagnostics13162637 |
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