A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images
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Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively.
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Khandakar et al. reported that a CNN and k-means clustering technique using thermogram images to classify thermograms based on severity of diabetic foot complications found that VGG 19 CNN model showed accuracy, precision, sensitivity, F1 score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively.
“it was found that the popular VGG 19 CNN model showed an accuracy, precision, sensitivity, F1 score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Khandakar et al.114
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Experimental — accuracy not yet measuredOne study classified thermogram images based on the severity of diabetic foot complications with 95.08% accuracy.
“For evaluation of diabetic foot, one study classified thermogram images based on the severity of diabetic foot complications (95.08% accuracy)114”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [114]
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
“For evaluation of diabetic foot, one study classified thermogram images based on the severity of diabetic foot complications (95.08% accuracy) 114 and the other study used smartphone images to estimate the likelihood of healing of diabetes-related foot ulcers (AUC = 0.73).”
MethodInfluentialArtificial intelligence for diabetes care: current and future prospects
Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., et al. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00154-2 · Cited by 106 · see citing works
Diabetes and its complications: molecular mechanisms, prevention and treatment
Zhao, L., Yuan, J., Yang, Q., Ma, J., Yang, F., Zou, Y., Liu, K., & Liu, F. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02401-w · Cited by 80 · see citing works
Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature
Khandakar, A. A., Mahmud, S., Chowdhury, M. E. H., Reaz, M. B. I., Serkan Kiranyaz, B., Mahbub, Z. B., Ali, S. H. M., Bakar, A. A. A., Ayari, M. A., Alhatou, M. I., et al. (2022) · Sensors · doi:10.3390/s22197599 · Cited by 80 · see citing works
Self-citationChemello, G., Salvatori, B., Morettini, M., & Tura, A. (2022) · Biosensors · doi:10.3390/bios12110985 · Cited by 60 · see citing works
The role of machine learning in advancing diabetic foot: a review
Guan, H., Wang, Y., Niu, P., Zhang, Y., Zhang, Y., Miao, R., Fang, X., Yin, R., Zhao, S., Liu, J., et al. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1325434 · Cited by 56 · see citing works
A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring
Kaselimi, M., Protopapadakis, E. E., Doulamis, A. D., & Doulamis, N. D. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.924546 · Cited by 38 · see citing works
Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead
Parab, R., Feeley, J. M., Valero, M., Chadalawada, L., Garcia, G. P., Kar, S. S., Madabhushi, A., Breton, M. D., Li, J., Shao, H., et al. (2025) · Endocrine Practice · doi:10.1016/j.eprac.2025.07.008 · Cited by 32 · see citing works
Arnia, F., Saddami, K., Roslidar, R., Muharar, R., & Munadi, K. (2024) · Smart Health · doi:10.1016/j.smhl.2024.100502 · Cited by 30 · see citing works
Khan, M. M., Chowdhury, M. E. H., Arefin, A. S. M. S., Podder, K. K., Hossain, M. S. A., Alqahtani, A., Murugappan, M., Khandakar, A. A., Mushtak, A., & Nahiduzzaman, M. (2023) · Diagnostics · doi:10.3390/diagnostics13152537 · Cited by 23 · see citing works
Self-citationHernández-Guedes, A., Arteaga-Marrero, N., Villa, E., Callicó, G. M., & Ruiz‐Alzola, J. (2023) · Sensors · doi:10.3390/s23020757 · Cited by 21 · see citing works
Chun, J., & Kim, H. (2023) · Journal of Korean Medical Science · doi:10.3346/jkms.2023.38.e253 · Cited by 21 · see citing works
Angiulli, G., Burrascano, P., Ricci, M., & Versaci, M. (2024) · Journal of Composites Science · doi:10.3390/jcs8120531 · Cited by 21 · see citing works
Munguía-Siu, A., Vergara, I., & Espinoza-Rodríguez, J. H. (2024) · Journal of Imaging · doi:10.3390/jimaging10120329 · Cited by 21 · see citing works
State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms
Arteaga-Marrero, N., Hernández-Guedes, A., Ortega‐Rodríguez, J., & Ruiz‐Alzola, J. (2023) · Biomedicines · doi:10.3390/biomedicines11123209 · Cited by 20 · see citing works
Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations
Xiaoling, W., Shengmei, Z., BingQian, W., Wen, L., Shuyan, G., Chen, H., Chenjie, Q., Dai, Y., & Li, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37635 · Cited by 19 · see citing works
Mısır, A. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251363632 · Cited by 18 · see citing works
Alhasson, H. F., & Alharbi, S. S. (2025) · Biomedicines · doi:10.3390/biomedicines13122928 · Cited by 18 · see citing works
Wartakusumah, R., Yamada, A., Noguchi, H., & Oe, M. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112446 · Cited by 16 · see citing works
PFUTnet: A Novel Deep Learning Architecture for Diabetic Foot Severity Mapping and Analysis
Sharma, N., Mirza, S., Rastogi, A., Mahapatra, P. K., & Kumar, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3377212 · Cited by 16 · see citing works
DSPNet: A Self-ONN Model for Robust DSPN Diagnosis From Temperature Maps
Khandakar, A. A., Chowdhury, M. E. H., Reaz, M. B. I., Serkan Kiranyaz, B., Hasan, A., Rahman, T., Ali, S. H. M., Shapiai, M. I., Bakar, A. A. A., Podder, K. K., et al. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3235252 · Cited by 15 · see citing works
Self-citationBeyond visual inspection: The value of infrared thermography in skin diseases, a scoping review
Speeckaert, R., Hoorens, I., Lambert, J., Speeckaert, M. M., & van Geel, N. (2024) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.19796 · Cited by 14 · see citing works
Diabetic wounds and artificial intelligence: A mini-review
Tehsin, S., Kausar, S., & Jameel, A. (2023) · World Journal of Clinical Cases · doi:10.12998/wjcc.v11.i1.84 · Cited by 14 · see citing works
Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection
Dhankhar, S., Garg, N., Chauhan, S., & Saini, M. (2024) · Current Biotechnology · doi:10.2174/0122115501303253240408072559 · Cited by 11 · see citing works
Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI
Lin, P.-C., Li, T.-C., Huang, T.-H., Hsu, Y., Ho, W., Xu, J.-L., Hsieh, C., & Jhang, Z.-E. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1613946 · Cited by 11 · see citing works
Use of thermal imaging in the detection of the diabetic foot- review
Mrowiec, A., Kasprzyk-Kucewicz, T., Wziątek-Kuczmik, D., Stanek, A., & Cholewka, A. (2025) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-025-09999-w · Cited by 10 · see citing works
Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
Islam, K. R., Kumar, J., Tan, T. L., Reaz, M. B. I., Rahman, T., Khandakar, A. A., Abbas, T. O., Hossain, M. S. A., Zughaier, S. M., & Chowdhury, M. E. H. (2022) · Diagnostics · doi:10.3390/diagnostics12092144 · Cited by 10 · see citing works
Self-citationDiabetes management in the era of artificial intelligence
Papazafiropoulou, A. K. (2024) · Archives of Medical Science - Atherosclerotic Diseases · doi:10.5114/amsad/183420 · Cited by 9 · see citing works
Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000–2023)
Ferdaus, J., Rochy, E. A., Biswas, U., Tiang, J. J., & Nahid, A.-A. (2024) · Sensors · doi:10.3390/s24165346 · Cited by 9 · see citing works
Pereira, M. G., Vilaça, M., Braga, D., Madureira, A. M., Silva, J. D., Santos, D., & Carvalho, E. (2023) · Wound Repair and Regeneration · doi:10.1111/wrr.13141 · Cited by 8 · see citing works
DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion
Wang, H., Wang, J., Xu, H., Sun, Y., & Yu, Z. (2022) · Sensors · doi:10.3390/s22145149 · Cited by 8 · see citing works
Digital management of diabetes global research trends: a bibliometric study
Zhu, S., Bian, H., Zhan, J., Ni, L., Huo, L., & Hu, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1620307 · Cited by 5 · see citing works
Ensemble of Vision Transformers and CNNs for Accurate Diabetic Foot Ulcer Classification
Pagadala, A. A., Silas, S., & Joy, E. (2024) · International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) · doi:10.1109/icc-robins60238.2024.10533993 · Cited by 5 · see citing works
Yasin, P., Dong, S., Aizezi, Z., Yimit, Y., Yusufu, A., Yakufu, M., & Song, X. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03236-9 · Cited by 5 · see citing works
Anggreni, N. K. I. S., Kristianto, H., Handayani, D., Yueniwati, Y., Irawan, P. L. T., Rosandi, R., Kapti, R. E., & Purnama, A. D. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251317521 · Cited by 4 · see citing works
Liao, H., Liao, T.-C., & Wu, Y.-F. (2026) · Advances in Wound Care · doi:10.1177/21621918261422388 · Cited by 4 · see citing works
Psychological and physiological computing based on multi-dimensional foot information
Li, S., Yao, H., Peng, R., Ma, Y., Zhang, B., Zhao, Z., Zhang, J., Chen, S., Wu, S., & Shu, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11087-5 · Cited by 4 · see citing works
Comparative Analysis: Deep vs. Machine Learning for Early DFU Detection in Medical Imaging
El-Kady, A. M. A. E.-A., Mostafa, M., Hussien, H. H. A., & Mousa, F. A. (2023) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa58542.2023.10217437 · Cited by 4 · see citing works
Artificial intelligence for diabetes management – a review
Maheshwari, S., Kalia, A., Tewari, J., Tewari, A., Srivastava, A., Dantu, R., Sachan, A. K., Verma, N., & Maheshwari, A. (2025) · Journal of Diabetes Metabolic Disorders & Control · doi:10.15406/jdmdc.2025.12.00292 · Cited by 3 · see citing works
Possibilities of medical thermal imaging in the organization of primary health care
Долгов, И. М., Воловик, М. Г., Zheleznyak, I. S., Karamyshev, Y. V., Karamysheva, A. A., Malakhovsky, V. N., & Makhnovskiy, A. I. (2023) · Medical alphabet · doi:10.33667/2078-5631-2023-7-42-50 · Cited by 3 · see citing works
Advantages of Thermovision Imaging for PPPM Approach to Diabetic Foot
Dębiec-Bąk, A., Skrzek, A., Ptak, A., Majerski, K., Uiberlayová, I., Stefańska, M., & Podbielska, H. (2023) · Advances in predictive, preventive and personalised medicine · doi:10.1007/978-3-031-34884-6_13 · Cited by 2 · see citing works
Pande, A., Kumar, A., & Anjankar, A. P. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025218 · Cited by 2 · see citing works
Can artificial intelligence and wearable technologies transform diabetes foot health?
Kong, W. M., & Saif, F. (2025) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000942 · Cited by 2 · see citing works
Das, I. J., Debata, J., Bhatta, K., Panda, S., & Samal, H. B. (2026) · Wound Repair and Regeneration · doi:10.1111/wrr.70187 · Cited by 2 · see citing works
Rosien, L., Geurten, R. J., Bilo, H. J. G., Ruwaard, D., Gans, R. O. B., Oskam, J., van Tilburg, C., Elissen, A., & van Dijk, P. R. (2024) · International Journal of Surgery Protocols · doi:10.1097/sp9.0000000000000027 · Cited by 2 · see citing works
Reverse Circular Logarithmic LBP for Diabetic Foot Ulcer Detection
Alevizos, V., Arampidis, N., Boja, I., & Papakostas, G. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76584-1_2 · Cited by 2 · see citing works
High-Accuracy Diabetic Foot Prediction Using Thermal Imaging and KNN-Based Machine Learning
Ali, A. A., Gharghan, S. K., & Ali, A. H. (2025) · Sensing and Imaging · doi:10.1007/s11220-025-00689-0 · Cited by 2 · see citing works
Emhandyksa, M., Soesanti, I., & Susilowati, R. (2023) · Jurnal Teknologi Informasi dan Ilmu Komputer · doi:10.25126/jtiik.1067382 · Cited by 2 · see citing works
Shandilya, G., Gupta, S. K., Gupta, D., Juneja, S., Chadaga, K., Nauman, A., & Al-Masri, A. A. (2026) · Scientific Reports · doi:10.1038/s41598-025-34225-7 · Cited by 1 · see citing works
Improving Diabetic Foot Care With Infrared Thermography and Artificial Intelligence: A Review
Teixeira, P., Filipe, V. M. D. J., & Teixeira, A. P. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261432639 · Cited by 1 · see citing works
Thermogram Based Diabetic Foot Ulcer Analysis Using Graph Neural Network
Chauhan, P., Kumar, M., Prakash, C., & Sikka, G. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5370-6_18 · Cited by 1 · see citing works
Mizzi, S., Mifsud, T., Mizzi, A., Borg, M. G., Farrugia, R., & Falzon, O. (2026) · Sensors · doi:10.3390/s26072119 · Cited by 1 · see citing works
Yang, L., Wang, Y., Wang, T., Zhou, J., Lv, J., & Liang, H. (2026) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-026-10041-w · Cited by 1 · see citing works
Wang, C., Zhang, Q., Wang, Y., Li, X., Yang, Y., Nie, S., Ge, S., Jiang, X., & ZHOU, J. (2026) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s620320 · Cited by 1 · see citing works
An automated system for the design of orthopaedic insoles
Marrone, F., Fabris, D. M., Galli, M., Giberti, H., Martino, M. D., Martino, G. D., & Tarabini, M. (2023) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea57477.2023.10171871 · Cited by 1 · see citing works
Review of Advancements in Noninvasive Detection Techniques of Foot Complications Due to Diabetes
Khandakar, A. A., Chowdhury, M. E. H., Reaz, M. B. I., Ali, S. H. M., Shapiai, M. I., Ayari, M. A., & Malik, R. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13244-5.00027-4 · Cited by 1 · see citing works
Self-citationArtificial intelligence/machine learning in the management of diabetes mellitus
Sonia, T. A., & Sharma, C. P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30272-5.00010-x · Cited by 1 · see citing works
Xu, S. (2026) · doi:10.2196/preprints.104385 · Cited by 1 · see citing works
Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images
Podder, K. K., Alam, M. K., Siam, Z. S., Islam, K. R., Dutta, P., Mushtak, A., Khandakar, A. A., Pedersen, S., & Chowdhury, M. E. H. (2023) · Studies in big data · doi:10.1007/978-981-99-3784-4_6
Self-citationArtificial Intelligence in Diabetes Mellitus Prediction: Advancements and Challenges - A Review
Awasthi, R., Mahavar, A., Shah, S., Patel, D. A., Patel, M., Shah, D. A., & Patel, A. D. (2024) · Current Bioinformatics · doi:10.2174/0115748936320525240915050640
Comparison of Type 2 Diabetes Mellitus on Foot Thermography Using Transfer Learning Techniques
Rajadevi, R., Logeswaran, K., Sathya, D., Rahshitha, K. S., Soumiya, K., & Pavithra, P. S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725864
Krishnan, N., & Balamurugan, V. (2024) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v11i11p121
Deep Learning‐Based Noninvasive Blood Glucose Estimation
Ali, S. M., Abbosh, Y. M., & Ali, D. M. (2025) · Journal of Engineering · doi:10.1155/je/1134023
Self-citationTrends in the use of artificial intelligence in the treatment of diabetic foot
Silva, B. M. (2025) · Podiatry (Buenos Aires) · doi:10.56294/pod2025152
Gil-Calvo, M., Jimenez‐Perez, I., Pérez‐Soriano, P., & Priego‐Quesada, J. I. (2025) · Springer eBooks · doi:10.1007/978-3-031-93311-0_14
Arunnagiri, A. M., & Pokkali, K. C. (2025) · Indian Journal of Science and Technology · doi:10.17485/ijst/v18i29.277
Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired AlgorithmsPreprint
Kataria, A., & Tiwari, R. (2025) · Research Square · doi:10.21203/rs.3.rs-7169643/v1
PREDICTION OF FOOT ULCER RECURRENCE IN ELDERLY DIABETIC PATIENTS BASED ON THE MAMBA MODEL
ZHANG, Y., Hong, S., & Lin, Y. (2025) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519425400937
Buderatska, N., Gontar, J. V., Yurchuk, A., Ilyin, I., Petrushko, M. P., & Yurchuk, T. O. (2025) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-025-03710-3
AI-Powered Innovations in Diabetes: A Narrative Review
Lai, P. K., & Nalliah, S. (2025) · Malaysian Journal of Medicine and Health Sciences · doi:10.47836/mjmhs.21.5.30
Klasifikasi Ulkus Kaki Diabetik Berbasis Transfer Learning: Studi Komparatif Arsitektur CNN
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Sohail, M., Yar, H., & Kim, H. S. (2026) · Mathematics · doi:10.3390/math14101666
A Systematic Review of Artificial Intelligence Techniques for Diabetic Neuropathy Detection
Chordiya, S., Dhole, A., Deshmukh, P. B., & Singh, M. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01434-5
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Exploring the Benefits of Digital Health Technologies in Diabetes Management
(2026) · Frontiers research topics · doi:10.3389/978-2-8325-8238-1
Aplicações da inteligência artificial no cuidado com o pé diabético: revisão de escopo
Kamada, R. V., Magalhães, S. R., Fracaroli, Y. R., Fava, S. M. C. L., Dázio, E. M. R., Costa, A. S., Costa, I. C. P., & Gomes, L. C. (2026) · Journal of Nursing and Health · doi:10.15210/jonah.v16i1.30565
The promise of artificial intelligence in diabetes management – a narrative review
Alfares, K. (2026) · Current Topics in Diabetes · doi:10.5114/ctd/221917
Smart Predictive Monitoring of Foot Pain with Machine Learning Algorithms
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- Authors
- Khandakar, A., Chowdhury, M. E. H., Reaz, M. B. I., Ali, S. H. M., Kiranyaz, S., Rahman, T., Chowdhury, M. H., Ayari, M. A., Alfkey, R., Bakar, A. A. A., Malik, R. A., & Hasan, A.
- Affiliations
- Qatar University (QA)
- National University of Malaysia (MY)
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Hamad Medical Corporation (QA)
- Weill Cornell Medical College in Qatar (QA)
- Corresponding author
- Chowdhury, Muhammad E. H.
- Reaz, Mamun Bin Ibne
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-06-02
- Volume
- 22
- Issue
- 11
- Pages
- 4249
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9185274
- OpenAlex
- W4281750928
- Cited by
- 74
- References
- 85
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 18 · 2023: 14 · 2022: 5
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Research Fund (QNRF) (NPRP12S-0227-190164, IRCC-2021-001, DPK-2021-001)
- International Research Collaboration Co-Fund (IRCC) (NPRP12S-0227-190164, IRCC-2021-001, DPK-2021-001)
- Universiti Kebangsaan Malaysia (NPRP12S-0227-190164, IRCC-2021-001, DPK-2021-001)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetic Foot Ulcer Assessment and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- diabetic foot, diabetic foot ulcer, infrared thermography, severity classification, plantar temperature, K-means clustering, stacking classifier, XGBoost, random forest, gradient boosting, diabetes complications
- MeSH terms
- Machine Learning, Algorithms, Diabetes Mellitus, Humans, Thermography, Neural Networks, Computer, Diabetic Foot
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