Classification of pressure ulcer images with logistic regression

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dc.authorscopusid57288894400
dc.authorscopusid56557054300
dc.authorscopusid57288894500
dc.authorscopusid15077642900
dc.contributor.authorYilmaz, B.
dc.contributor.authorAtagun, E.
dc.contributor.authorDemircan, F. O.
dc.contributor.authorYucedag, I.
dc.date.accessioned2021-12-01T18:38:53Z
dc.date.available2021-12-01T18:38:53Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.descriptionKocaeli University;Kocaeli University Technoparken_US
dc.description2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175en_US
dc.description.abstractPressure ulcers are wounds caused by prolonged lying in bedridden patients. This has become an important health problem in many countries. Correct diagnosis of pressure ulcers is very important for an effective treatment method. The characteristics of these wounds are effective in terms of seeing the healing. Interventional methods of obtaining information in the diagnosis of pressure ulcers are painful for patients. In addition, these methods can increase the risk of infection. Therefore, imaging systems such as nonsurgical wound tracking techniques allow accurate analysis of the features of the wound without contact with it. The aim of this study is to prevent wound formation or to make a positive contribution to the treatment processes by using machine learning techniques in image analysis for the classification of pressure ulcers. In this study, 142 wound images were analyzed by Logistic Regression and Artificial Neural Networks methods. Features such as wound color and size in these images were separated by image processing and the stage of the wound was determined from the images. The 6 stages of pressure ulcers are referenced for classification. © 2021 IEEE.en_US
dc.identifier.doi10.1109/INISTA52262.2021.9548585
dc.identifier.isbn9781665436038
dc.identifier.scopus2-s2.0-85116607764en_US
dc.identifier.urihttps://doi.org/10.1109/INISTA52262.2021.9548585
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9890
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectImage classificationen_US
dc.subjectLogistic regressionen_US
dc.subjectPressure ulceren_US
dc.titleClassification of pressure ulcer images with logistic regressionen_US
dc.typeConference Objecten_US

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