A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images
Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using therm...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/15/7524 |
| _version_ | 1827626123570184192 |
|---|---|
| author | Khairul Munadi Khairun Saddami Maulisa Oktiana Roslidar Roslidar Kahlil Muchtar Melinda Melinda Rusdha Muharar Maimun Syukri Taufik Fuadi Abidin Fitri Arnia |
| author_facet | Khairul Munadi Khairun Saddami Maulisa Oktiana Roslidar Roslidar Kahlil Muchtar Melinda Melinda Rusdha Muharar Maimun Syukri Taufik Fuadi Abidin Fitri Arnia |
| author_sort | Khairul Munadi |
| collection | DOAJ |
| description | Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier. |
| first_indexed | 2024-03-09T12:49:28Z |
| format | Article |
| id | doaj.art-d36736e9927144dfb70c2ca8d028e0c9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:49:28Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d36736e9927144dfb70c2ca8d028e0c92023-11-30T22:09:15ZengMDPI AGApplied Sciences2076-34172022-07-011215752410.3390/app12157524A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal ImagesKhairul Munadi0Khairun Saddami1Maulisa Oktiana2Roslidar Roslidar3Kahlil Muchtar4Melinda Melinda5Rusdha Muharar6Maimun Syukri7Taufik Fuadi Abidin8Fitri Arnia9Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaMedical Faculty, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Informatics, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDepartment of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, IndonesiaDiabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier.https://www.mdpi.com/2076-3417/12/15/7524diabetic foot ulcer (DFU)deep neural networksdecision fusionMobileNetV2ShuffleNet |
| spellingShingle | Khairul Munadi Khairun Saddami Maulisa Oktiana Roslidar Roslidar Kahlil Muchtar Melinda Melinda Rusdha Muharar Maimun Syukri Taufik Fuadi Abidin Fitri Arnia A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images Applied Sciences diabetic foot ulcer (DFU) deep neural networks decision fusion MobileNetV2 ShuffleNet |
| title | A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images |
| title_full | A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images |
| title_fullStr | A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images |
| title_full_unstemmed | A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images |
| title_short | A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images |
| title_sort | deep learning method for early detection of diabetic foot using decision fusion and thermal images |
| topic | diabetic foot ulcer (DFU) deep neural networks decision fusion MobileNetV2 ShuffleNet |
| url | https://www.mdpi.com/2076-3417/12/15/7524 |
| work_keys_str_mv | AT khairulmunadi adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT khairunsaddami adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT maulisaoktiana adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT roslidarroslidar adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT kahlilmuchtar adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT melindamelinda adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT rusdhamuharar adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT maimunsyukri adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT taufikfuadiabidin adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT fitriarnia adeeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT khairulmunadi deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT khairunsaddami deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT maulisaoktiana deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT roslidarroslidar deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT kahlilmuchtar deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT melindamelinda deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT rusdhamuharar deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT maimunsyukri deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT taufikfuadiabidin deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages AT fitriarnia deeplearningmethodforearlydetectionofdiabeticfootusingdecisionfusionandthermalimages |