Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4632
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dc.contributor.authorEldem, H.-
dc.contributor.authorÜlker, E.-
dc.contributor.authorIşıklı, O.Y.-
dc.date.accessioned2023-10-02T11:17:36Z-
dc.date.available2023-10-02T11:17:36Z-
dc.date.issued2023-
dc.identifier.issn2215-0986-
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2023.101490-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4632-
dc.description.abstractIn medical world, wound care and follow-up is one of the issues that are gaining importance to work on day by day. Accurate and early recognition of wounds can reduce treatment costs. In the field of computer vision, deep learning architectures have received great attention recently. The achievements of existing pre-trained architectures for describing (classifying) data belonging to many image sets in the real world are primarily addressed. However, to increase the success of these architectures in a certain area, some improvements and enhancements can be made on the architecture. In this paper, the classification of pressure and diabetic wound images was performed with high accuracy. The six different new AlexNet architecture variations (3Conv_Softmax, 3Conv_SVM, 4Conv_Softmax, 4Conv_SVM, 6Conv_Softmax, 6Conv_SVM) were created with a different number of implementations of Convolution, Pooling, and Rectified Linear Activation (ReLU) layers. Classification performances of the proposed models are investigated by using Softmax classifier and SVM classifier separately. A new original Wound Image Database are created for performance measures. According to the experimental results obtained for the Database, the model with 6 Convolution layers (6Conv_SVM) was the most successful method among the proposed methods with 98.85% accuracy, 98.86% sensitivity, and 99.42% specificity. The 6Conv_SVM model was also tested on diabetic and pressure wound images in the public medetec dataset, and 95.33% accuracy, 95.33% sensitivity, and 97.66% specificity values were obtained. The proposed method provides high performance compared to the pre-trained AlexNet architecture and other state-of-the-art models in the literature. The results showed that the proposed 6Conv_SVM architecture can be used by the relevant departments in the medical world with good performance in medical tasks such as examining and classifying wound images and following up the wound process. © 2023 Karabuk Universityen_US
dc.description.sponsorshipThis study was supported by the Scientific Research Project at Konya Technical University, Konya, Turkey (No. 231113005).en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofEngineering Science and Technology, an International Journalen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional Neural Networks (CNN); Deep learning; Modified AlexNet; Transfer learning; Wound image classificationen_US
dc.titleAlexnet architecture variations with transfer learning for classification of wound imagesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.jestch.2023.101490-
dc.identifier.scopus2-s2.0-85166305764en_US
dc.departmentKTÜNen_US
dc.identifier.volume45en_US
dc.identifier.wosWOS:001143030400001en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid55326495100-
dc.authorscopusid23393979800-
dc.authorscopusid55367909400-
dc.identifier.scopusqualityQ1-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.cerifentitytypePublications-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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