Paradigm shift from Artificial Neural Networks (ANNs) to deep Convolutional Neural Networks (DCNNs) in the field of medical image processing

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dc.authoridAbut, Serdar/0000-0002-6617-6688
dc.authoridOkut, Hayrettin/0000-0003-4084-8404
dc.contributor.authorAbut, Serdar
dc.contributor.authorOkut, Hayrettin
dc.contributor.authorKallail, K. James
dc.date.accessioned2024-12-24T19:27:03Z
dc.date.available2024-12-24T19:27:03Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractImages and other types of unstructural data in the medical domain are rapidly becoming data-intensive. Actionable insights from these complex data present new opportunities but also pose new challenges for classification or segmentation of unstructural data sources. Over the years, medical problems have been solved by combining traditional statistical methods with image processing methods. Both the increase in the size of the data and the increase in the resolution are among the factors that shape the ongoing improvements in artificial intelligence (AI), particularly concerning deep learning (DL) techniques for evaluation of these medical data to identify, classify, and quantify patterns for clinical needs. At this point, it is important to understand how Artificial Neural Networks (ANNs), which are an important milestone in interpreting big data, transform into Deep Convolutional Neural Networks (DCNNs) and to predict where the change will go. We aimed to explain the needs of these stages in medical image processing through the studies in the literature. At the same time, information is provided about the studies that lead to paradigm shift and try to solve the image related medical problems by using DCNNs. With the increase in the knowledge of medical doctors on this subject, it will be possible to look at the solution of new problems in computer science from different perspectives.
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) [1059B192100853]
dc.description.sponsorshipSerdar Abut acknowledges a Postdoctoral grant from the Scientific and Technical Research Council of Turkey (TUBITAK, 2219-International Postdoctoral Research Scholarship Programme, Grant Number: 1059B192100853) .
dc.identifier.doi10.1016/j.eswa.2023.122983
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85180749408
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.122983
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6487
dc.identifier.volume244
dc.identifier.wosWOS:001143147800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectDeep Convolutional Neural Networks
dc.subjectMedical Image Processing
dc.subjectArtificial Neural Networks
dc.subjectFeature Extraction
dc.titleParadigm shift from Artificial Neural Networks (ANNs) to deep Convolutional Neural Networks (DCNNs) in the field of medical image processing
dc.typeReview Article

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