Segmentation of histopathological images with Convolutional Neural Networks using Fourier features

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dc.authorscopusid26657939200
dc.authorscopusid8362224100
dc.contributor.authorHatipo?lu N.
dc.contributor.authorBilgin G.
dc.date.accessioned2024-06-12T10:25:24Z
dc.date.available2024-06-12T10:25:24Z
dc.date.issued2015
dc.description2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052en_US
dc.description.abstractThe study aims to boost the success of the segmentation results by evaluating spatial relations in the segmentation of histopathalogical images. In the first step Fourier features are extracted from RGB color space of digital histopathalogical images. Training data sets are formed by selecting equal number of different cellular and extra-cellular structures in spatial domain from the images. Classification models of each training data set is obtained by utilizing Convolutional Neural Network (CNN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) methods. Visual and numerical outputs which are obtained from supervised training methods are presented for comparison purpose in the experimental results section. © 2015 IEEE.en_US
dc.identifier.doi10.1109/SIU.2015.7129857
dc.identifier.endpage458en_US
dc.identifier.isbn9.78147E+12
dc.identifier.scopus2-s2.0-84939138652en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage455en_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2015.7129857
dc.identifier.urihttps://hdl.handle.net/20.500.14551/16332
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Network; Fourier Transform; Histopathologic Images; Segmentation; Spatial Relationsen_US
dc.subjectClassification (Of Information); Convolution; Fourier Transforms; Image Segmentation; Nearest Neighbor Search; Numerical Methods; Support Vector Machines; Classification Models; Histopathologic Images; Histopathological Images; K-Nearest Neighbors; Segmentation Results; Spatial Relations; Supervised Trainings; Training Data Sets; Convolutional Neural Networksen_US
dc.titleSegmentation of histopathological images with Convolutional Neural Networks using Fourier featuresen_US
dc.title.alternativeHistopatolojik Görüntülerde Fourier Özellikleri Kullanilarak Evrişim Yapay Sinir A?i ile Bölütlemeen_US
dc.typeConference Objecten_US

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