{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:45:32Z","timestamp":1791855932871,"version":"4.3.4"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2023R259"],"award-info":[{"award-number":["PNURSP2023R259"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2013patch 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\u2013patch thermograms.<\/jats:p>","DOI":"10.3390\/diagnostics13162637","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"2637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-9358","authenticated-orcid":false,"given":"Ikramullah","family":"Khosa","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Awais","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Anjum","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waseem","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut 250005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0254-3351","authenticated-orcid":false,"given":"Sana","family":"Shahab","sequence":"additional","affiliation":[{"name":"Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126964","DOI":"10.1016\/j.jhydrol.2021.126964","article-title":"Exploring the Utility of Radar and Satellite-Sensed Precipitation and Their Dynamic Bias Correction for Integrated Prediction of Flood and Landslide Hazards","volume":"603","author":"Wang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.2337\/dc09-0651","article-title":"History of Foot Ulcer Increases Mortality Among Individuals with DiabetesTen-Year Follow-up of the Nord-Tr\u00f8ndelag Health Study, Norway","volume":"32","author":"Iversen","year":"2009","journal-title":"Diabetes Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161296","DOI":"10.1109\/ACCESS.2019.2951356","article-title":"Plantar Thermogram Database for the Study of Diabetic Foot Complications","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1177\/193229681000400414","article-title":"Thermal Imaging Today and Its Relevance to Diabetes","volume":"4","author":"Ring","year":"2010","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1177\/193229681000400613","article-title":"Correlation between Plantar Foot Temperature and Diabetic Neuropathy: A Case Study by Using an Infrared Thermal Imaging Technique","volume":"4","author":"Bagavathiappan","year":"2010","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.infrared.2016.07.013","article-title":"Narrative Review: Diabetic Foot and Infrared Thermography","volume":"78","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","first-page":"102","article-title":"Infrared Dermal Thermography on Diabetic Feet Soles to Predict Ulcerations: A Case Study","volume":"8572","author":"Liu","year":"2013","journal-title":"Adv. Biomed. Clin. Diagn. Syst. XI"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.infrared.2014.06.001","article-title":"Application of Infrared Thermography in Computer Aided Diagnosis","volume":"66","author":"Faust","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cruz-Vega, I., Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J.d.J., and Ramirez-Cortes, J.M. (2020). Deep Learning Classification for Diabetic Foot Thermograms. Sensors, 20.","DOI":"10.3390\/s20061762"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.infrared.2018.06.010","article-title":"Automated Detection of Diabetic Foot with and without Neuropathy Using Double Density-Dual Tree-Complex Wavelet Transform on Foot Thermograms","volume":"92","author":"Adam","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/TETCI.2018.2866254","article-title":"DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification","volume":"4","author":"Goyal","year":"2018","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1007\/s11517-022-02617-w","article-title":"Machine Learning Algorithm to Evaluate Risk Factors of Diabetic Foot Ulcers and Its Severity","volume":"60","author":"Nanda","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18887","DOI":"10.1007\/s11042-022-14101-6","article-title":"Nested Segmentation and Multi-Level Classification of Diabetic Foot Ulcer Based on Mask R-CNN","volume":"82","author":"Cao","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/s12938-017-0408-x","article-title":"Diabetic Foot Ulcer Mobile Detection System Using Smart Phone Thermal Camera: A Feasibility Study","volume":"16","author":"Fraiwan","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"811028","DOI":"10.3389\/fbioe.2021.811028","article-title":"Classification of Diabetic Foot Ulcers Using Class Knowledge Banks","volume":"9","author":"Xu","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1007\/s13246-022-01185-5","article-title":"ACTNet: Asymmetric Convolutional Transformer Network for Diabetic Foot Ulcers Classification","volume":"45","author":"Ai","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/JBHI.2018.2868656","article-title":"Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices","volume":"23","author":"Goyal","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Santos, F., Santos, E., Vogado, L.H., Ito, M., Bianchi, A., Tavares, J.M., and Veras, R. (2022, January 1\u20133). DFU-VGG, a Novel and Improved VGG-19 Network for Diabetic Foot Ulcer Classification. Proceedings of the 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP), Sofia, Bulgaria.","DOI":"10.1109\/IWSSIP55020.2022.9854392"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105055","DOI":"10.1016\/j.compbiomed.2021.105055","article-title":"Diabetic Foot Ulcer Classification Using Mapped Binary Patterns and Convolutional Neural Networks","volume":"140","author":"Ebsim","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"198977","DOI":"10.1109\/ACCESS.2020.3035327","article-title":"Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review","volume":"8","author":"Tulloch","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Niri, R., Lucas, Y., Treuillet, S., and Douzi, H. (2019). Journ\u00e9es d\u2019Etude sur la T\u00e9l\u00e9Sant\u00e9, Sorbonne Universit\u00e9s."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"026003","DOI":"10.1117\/1.JBO.20.2.026003","article-title":"Automatic Detection of Diabetic Foot Complications with Infrared Thermography by Asymmetric Analysis","volume":"20","author":"Liu","year":"2015","journal-title":"J. Biomed. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vardasca, R., Magalhaes, C., Seixas, A., Carvalho, R., and Mendes, J. (2019, January 1\u20135). Diabetic Foot Monitoring Using Dynamic Thermography and AI Classifiers. Proceedings of the 3rd Quantitative InfraRed Thermography Asia Conference (QIRT), Tokyo, Japan.","DOI":"10.21611\/qirt.2019.027"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.infrared.2018.01.022","article-title":"Automated Characterization of Diabetic Foot Using Nonlinear Features Extracted from Thermograms","volume":"89","author":"Adam","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1177\/193229681300700504","article-title":"Infrared Thermal Imaging for Automated Detection of Diabetic Foot Complications","volume":"7","author":"Liu","year":"2013","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1089\/dia.2014.0052","article-title":"Diagnostic Values for Skin Temperature Assessment to Detect Diabetes-Related Foot Complications","volume":"16","author":"Prijs","year":"2014","journal-title":"Diabetes Technol. Ther."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Khosa, I., and Pasero, E. (2014). Defect Detection in Food Ingredients Using Multilayer Perceptron Neural Network, IEEE.","DOI":"10.1109\/WSCAR.2014.6916782"},{"key":"ref_28","first-page":"18","article-title":"SVMs-A Practical Consequence of Learning Theory","volume":"13","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/T-C.1975.224110","article-title":"An Algorithm for Finding Nearest Neighbors","volume":"C-24","author":"Baskett","year":"1975","journal-title":"IEEE Trans. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cichosz, P. (2015). Data Min. Algorithms, Wiley Data and Cybersecurity.","DOI":"10.1002\/9781118950951"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1023\/A:1007618119488","article-title":"Soft Margins for AdaBoost","volume":"42","author":"Onoda","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/11499145_13","article-title":"Image Analysis with Local Binary Patterns","volume":"3540","year":"2005","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_36","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/BF00204594","article-title":"Gabor Filters as Texture Discriminator","volume":"61","author":"Fogel","year":"1989","journal-title":"Biol. Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_39","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/16\/2637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:30:01Z","timestamp":1760128201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/16\/2637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["diagnostics13162637"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13162637","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}