Convolution Kernel Size Effect on Convolutional Neural Network in Histopathological Image Processing Applications

Basit Öğe Kaydı
dc.contributor.authorOzturk, Saban
dc.contributor.authorOzkaya, Umut
dc.contributor.authorAkdemir, Bayram
dc.contributor.authorSeyfi, Levent
dc.date.accessioned2020-03-26T19:53:14Z
dc.date.available2020-03-26T19:53:14Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Symposium on Fundamentals of Electrical Engineering (ISFEE) -- NOV 01-03, 2018 -- Univ Politehnica Bucharest, Fac Elect Engn, Elect Engn Dept, Bucharest, ROMANIAen_US
dc.description.abstractIn this study, the change in the classification success of the convolutional neural network (CNN) is investigated when the dimensions of the convolution window are altered. For this purpose, four different linear convolution neural network architectures are constructed. The first architecture includes 4 convolution layers with 3x3 convolution window dimensions. The second architecture includes 4 convolution layers with 5x5 convolution window dimensions. The third architecture includes 4 convolution layers with 7x7 convolution window dimensions. The fourth architecture includes 4 convolution layers with 9x9 convolution window dimensions. A dataset consisting of histopathological image patches is used to test the CNN architects that are created. 2000 training images and 250 validation images on dataset are applied to all architectures with the same order, in order to fair assessment. In conclusion, the effect of convolution dimensions on classification of histopathological images by deep learning methods is determined. The test results of four different linear convolutional neural network architectures are evaluated using sensitivity, specificity and accuracy parameters.en_US
dc.description.sponsorshipAssoc Romanian Elect Elect Engineers, IEEE Romania Sect CAS CS Chapter, IEEEen_US
dc.identifier.isbn978-1-5386-7212-9
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36445
dc.identifier.wosWOS:000480396400076en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 INTERNATIONAL SYMPOSIUM ON FUNDAMENTALS OF ELECTRICAL ENGINEERING (ISFEE)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjecthistopathological imageen_US
dc.subjectconvolutional neural networksen_US
dc.subjectCNNen_US
dc.subjectclassificationen_US
dc.subjectwhole-slideen_US
dc.titleConvolution Kernel Size Effect on Convolutional Neural Network in Histopathological Image Processing Applicationsen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu