Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images

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dc.authorid0000-0003-3726-5945en_US
dc.authorid0000-0002-6054-9244
dc.authoridBAİBÜ, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü
dc.contributor.authorÖzkan, Murat
dc.contributor.authorÇakıroğlu, Murat
dc.contributor.authorKocaman, Orhan
dc.contributor.authorKurt, Mevlüt
dc.contributor.authorYılmaz, Bülent
dc.contributor.authorCan, Güray
dc.contributor.authorKorkmaz, Uğur
dc.date.accessioned2021-06-23T19:43:46Z
dc.date.available2021-06-23T19:43:46Z
dc.date.issued2016
dc.departmentBAİBÜ, Bolu Teknik Bilimler Meslek Yüksekokulu, Elektrik Ve Enerji Bölümüen_US
dc.description.abstractAim: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images. Materials and Methods: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients. Results: 122 features were identified from the 332 endosonography images obtained in the study, and the 20 most appropriate features were selected by using the relief method. Images classified under three age groups (in years; <40, 40-60 and >60) were tested via 200 random tests and the following ratios were obtained in the classification: accuracy: 92%, 88.5%, and 91.7%, respectively; sensitivity: 87.5%, 85.7%, and 93.3%, respectively; and specificity: 94.1%, 91.7%, and 88.9%, respectively. When all the age groups were assessed together, the following values were obtained: accuracy: 87.5%, sensitivity: 83.3%, and specificity: 93.3%. Conclusions: It was observed that the CAD system developed in the study performed better in diagnosing pancreatic cancer images based on classification by patient age compared to diagnosis without classification. Therefore, it is imperative to take patient age into consideration to ensure higher performance.en_US
dc.identifier.doi10.4103/2303-9027.180473
dc.identifier.endpage107en_US
dc.identifier.issn2303-9027
dc.identifier.issn2226-7190
dc.identifier.issue2en_US
dc.identifier.pmid27080608en_US
dc.identifier.scopus2-s2.0-85002145930en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage101en_US
dc.identifier.urihttps://doi.org/10.4103/2303-9027.180473
dc.identifier.urihttps://hdl.handle.net/20.500.12491/8849
dc.identifier.volume5en_US
dc.identifier.wosWOS:000374959900006en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorÖzkan, Murat
dc.institutionauthorKurt, Mevlüt
dc.institutionauthorYılmaz, Bülent
dc.institutionauthorCan, Güray
dc.institutionauthorKorkmaz, Uğur
dc.language.isoenen_US
dc.publisherWolters Kluwer Medknow Publicationsen_US
dc.relation.ispartofEndoscopic Ultrasounden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer-Aided Diagnosis (CAD)en_US
dc.subjectEndoscopic Ultrasound (EUS) Imagesen_US
dc.subjectPancreatic Canceren_US
dc.titleAge-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound imagesen_US
dc.typeArticleen_US

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