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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| مؤلفون آخرون: | , , , , , , , , , , , |
| منشور في: |
2022
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| _version_ | 1864513517945618432 |
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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 |