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Fractal analysis of thyroid ultrasound image data evaluation

dc.contributor.authorBayrak, E. A.
dc.contributor.authorKırcı, P.
dc.contributor.buuauthorKIRCI, PINAR
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentBilgisayar Mühendisliği Bölümü
dc.contributor.scopusid15026635000
dc.date.accessioned2025-05-13T09:12:35Z
dc.date.issued2020-10-05
dc.description.abstractThe prediction or early diagnosis of thyroid diseases is really important for healthy life of people. Computer aid diagnosis (CAD) system and ultrasound diagnostic technology can be pretty useful for any treatment thyroid diseases such as lesion, cyst and nodule. In our study is used fractal analysis that can be said as a branch of CAD system and ultrasound diagnostic technology, for analyzing thyroid ultrasound image data. The growth of any lesion or nodule on thyroid can be estimated from fractal analysis on ultrasound images of a thyroid patient. Fractal analysis used as an ultrasound diagnostic technology can help in preventing redundant needle biopsy to patients who have benign thyroid nodules.
dc.identifier.doi10.1109/SAIC51296.2020.9239183
dc.identifier.isbn978-172819082-2
dc.identifier.scopus2-s2.0-85097147637
dc.identifier.urihttps://hdl.handle.net/11452/51985
dc.indexed.scopusScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.journal2020 IEEE 2nd International Conference on System Analysis and Intelligent Computing, SAIC 2020
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectUltrasound diagnostic technology
dc.subjectFractal analysis
dc.subjectComputer aid diagnosis
dc.subject.scopusDeep Learning; Ultrasound Image; Ultrasonics
dc.titleFractal analysis of thyroid ultrasound image data evaluation
dc.typeconferenceObject
dc.type.subtypeConference Paper
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü
local.indexed.atScopus
relation.isAuthorOfPublication0270c3e7-f379-4f0e-84dd-a83c2bbf0235
relation.isAuthorOfPublication.latestForDiscovery0270c3e7-f379-4f0e-84dd-a83c2bbf0235

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