{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:48:35Z","timestamp":1775328515581,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.<\/jats:p>","DOI":"10.3390\/a15070236","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T05:41:06Z","timestamp":1657086066000},"page":"236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Automatic Classification of Foot Thermograms Using Machine Learning Techniques"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"first","affiliation":[{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"},{"name":"INESC TEC\u2014INESC Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9955-1977","authenticated-orcid":false,"given":"Pedro","family":"Teixeira","sequence":"additional","affiliation":[{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"}]},{"given":"Ana","family":"Teixeira","sequence":"additional","affiliation":[{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal"},{"name":"Mathematics Centre CMAT, Pole CMAT\u2014UTAD, Quinta de Prados, 5001-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/42.746635","article-title":"A Reappraisal of the Use of Infrared Thermal Image Analysis in Medicine","volume":"17","author":"Jones","year":"1998","journal-title":"IEEE Trans. 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