Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

<div><p>Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches ap...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Amith Khandakar (14151981) (author)
مؤلفون آخرون: Muhammad E. H. Chowdhury (14150526) (author), Mamun Bin Ibne Reaz (16875933) (author), Sawal Hamid Md Ali (18441105) (author), Tariq O. Abbas (11247771) (author), Tanvir Alam (638619) (author), Mohamed Arselene Ayari (16869978) (author), Zaid B. Mahbub (18441108) (author), Rumana Habib (16904892) (author), Tawsifur Rahman (14150523) (author), Anas M. Tahir (16870077) (author), Ahmad Ashrif A. Bakar (16904889) (author), Rayaz A. Malik (7372649) (author)
منشور في: 2022
الموضوعات:
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author Amith Khandakar (14151981)
author2 Muhammad E. H. Chowdhury (14150526)
Mamun Bin Ibne Reaz (16875933)
Sawal Hamid Md Ali (18441105)
Tariq O. Abbas (11247771)
Tanvir Alam (638619)
Mohamed Arselene Ayari (16869978)
Zaid B. Mahbub (18441108)
Rumana Habib (16904892)
Tawsifur Rahman (14150523)
Anas M. Tahir (16870077)
Ahmad Ashrif A. Bakar (16904889)
Rayaz A. Malik (7372649)
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author_facet Amith Khandakar (14151981)
Muhammad E. H. Chowdhury (14150526)
Mamun Bin Ibne Reaz (16875933)
Sawal Hamid Md Ali (18441105)
Tariq O. Abbas (11247771)
Tanvir Alam (638619)
Mohamed Arselene Ayari (16869978)
Zaid B. Mahbub (18441108)
Rumana Habib (16904892)
Tawsifur Rahman (14150523)
Anas M. Tahir (16870077)
Ahmad Ashrif A. Bakar (16904889)
Rayaz A. Malik (7372649)
author_role author
dc.creator.none.fl_str_mv Amith Khandakar (14151981)
Muhammad E. H. Chowdhury (14150526)
Mamun Bin Ibne Reaz (16875933)
Sawal Hamid Md Ali (18441105)
Tariq O. Abbas (11247771)
Tanvir Alam (638619)
Mohamed Arselene Ayari (16869978)
Zaid B. Mahbub (18441108)
Rumana Habib (16904892)
Tawsifur Rahman (14150523)
Anas M. Tahir (16870077)
Ahmad Ashrif A. Bakar (16904889)
Rayaz A. Malik (7372649)
dc.date.none.fl_str_mv 2022-02-24T03:00:00Z
dc.identifier.none.fl_str_mv 10.3390/s22051793
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Thermal_Change_Index-Based_Diabetic_Foot_Thermogram_Image_Classification_Using_Machine_Learning_Techniques/25688778
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Engineering
Biomedical engineering
Information and computing sciences
Machine learning
diabetic foot
thermogram
thermal change index
machine learning
deep learning
dc.title.none.fl_str_mv Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <div><p>Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22051793" target="_blank">https://dx.doi.org/10.3390/s22051793</a></p>
eu_rights_str_mv openAccess
id Manara2_3884e20735c5b6f0b3fbb43ba13a37e9
identifier_str_mv 10.3390/s22051793
network_acronym_str Manara2
network_name_str Manara2
oai_identifier_str oai:figshare.com:article/25688778
publishDate 2022
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning TechniquesAmith Khandakar (14151981)Muhammad E. H. Chowdhury (14150526)Mamun Bin Ibne Reaz (16875933)Sawal Hamid Md Ali (18441105)Tariq O. Abbas (11247771)Tanvir Alam (638619)Mohamed Arselene Ayari (16869978)Zaid B. Mahbub (18441108)Rumana Habib (16904892)Tawsifur Rahman (14150523)Anas M. Tahir (16870077)Ahmad Ashrif A. Bakar (16904889)Rayaz A. Malik (7372649)EngineeringBiomedical engineeringInformation and computing sciencesMachine learningdiabetic footthermogramthermal change indexmachine learningdeep learning<div><p>Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22051793" target="_blank">https://dx.doi.org/10.3390/s22051793</a></p>2022-02-24T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s22051793https://figshare.com/articles/journal_contribution/Thermal_Change_Index-Based_Diabetic_Foot_Thermogram_Image_Classification_Using_Machine_Learning_Techniques/25688778CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256887782022-02-24T03:00:00Z
spellingShingle Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
Amith Khandakar (14151981)
Engineering
Biomedical engineering
Information and computing sciences
Machine learning
diabetic foot
thermogram
thermal change index
machine learning
deep learning
status_str publishedVersion
title Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
title_full Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
title_fullStr Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
title_full_unstemmed Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
title_short Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
title_sort Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
topic Engineering
Biomedical engineering
Information and computing sciences
Machine learning
diabetic foot
thermogram
thermal change index
machine learning
deep learning