HIC-net: A deep convolutional neural network model for classification of histopathological breast images

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dc.contributor.authorÖztürk, Şaban
dc.contributor.authorAkdemir, Bayram
dc.date.accessioned2020-03-26T20:14:30Z
dc.date.available2020-03-26T20:14:30Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture performs window-based classification by dividing the WSI into a certain plane. In our method, an effective pre-processing step has been added for WSI for better predictability of image parts and faster training. A large dataset containing 30,656 images is used for the evaluation of the HIC-net algorithm. Of these images, 23,040 are used for training, 2560 are used for validation and 5056 are used for testing. HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%. If we evaluate the classification results of HIC-net using softmax function, HIC-net success rates have 96.71% sensitivity, 95.7% specificity, 96.21% accuracy, and are more successful than other state-of-the-art techniques which are used in cancer research. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipTUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThis study was funded by TUBITAK.en_US
dc.identifier.citationÖztürk, Ş., Akdemir, B. (2019). HIC-net: A Deep Convolutional Neural Network Model for Classification of Histopathological Breast Images. Computers and Electrical Engineering, 76, 299-310.
dc.identifier.doi10.1016/j.compeleceng.2019.04.012en_US
dc.identifier.endpage310en_US
dc.identifier.issn0045-7906en_US
dc.identifier.issn1879-0755en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage299en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.compeleceng.2019.04.012
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37888
dc.identifier.volume76en_US
dc.identifier.wosWOS:000470954900024en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAkdemir, Bayram.
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofCOMPUTERS & ELECTRICAL ENGINEERINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCancer classificationen_US
dc.subjectConvolutional neural networksen_US
dc.subjectCNNen_US
dc.subjectHistopathological imageen_US
dc.subjectWhole-slideen_US
dc.titleHIC-net: A deep convolutional neural network model for classification of histopathological breast imagesen_US
dc.typeArticleen_US

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