Attention-based end-to-end CNN framework for content-based X-ray image retrieval

Basit Öğe Kaydı
dc.authorid0000-0003-2371-8173en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorÖztürk, Şaban
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-07-03T07:59:10Z
dc.date.available2023-07-03T07:59:10Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionScientific and Technological Research Council of Turkey (TUBITAK)(Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK))en_US
dc.description.abstractThe widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access to similar images from a large X-ray dataset is presented. In the first part of the proposed framework, a fully convolutional network architecture with attention structures is presented. This section contains several layers for determining the saliency points of X-ray images. In the second part of the framework, the modified image with X-ray saliency map is converted to representative codes in Euclidean space by the ResNet-18 architecture. Finally, hash codes are obtained by transforming these codes into hamming spaces. The proposed study is superior in terms of high performance and customized layers compared to current state-of-the-art X-ray image retrieval methods in the literature. Extensive experimental studies reveal that the proposed framework can increase the current precision performance by up to 13en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [120E018]en_US
dc.identifier.citationÖztürk, Ş., Alhudhaif, A., & Polat, K. (2021). Attention-based end-to-end CNN framework for content-based x-ray imageretrieval. Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), 2680-2693.en_US
dc.identifier.doi10.3906/elk-2105-242
dc.identifier.endpage2693en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.scopus2-s2.0-85117241123en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2680en_US
dc.identifier.trdizinid526850en_US
dc.identifier.urihttp://dx.doi.org/10.3906/elk-2105-242
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11220
dc.identifier.volume29en_US
dc.identifier.wosWOS:000706889700002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.tubitak[120E018]
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectX-Rayen_US
dc.subjectAttentionen_US
dc.subjectRetrievalen_US
dc.subjectHashen_US
dc.subjectCNNen_US
dc.subjectClassificationen_US
dc.titleAttention-based end-to-end CNN framework for content-based X-ray image retrievalen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
saban-ozturk.pdf
Boyut:
3.16 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin/Full text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu