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Deep learning-based histological scoring of cerulein-induced acute pancreatitis rat model

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dc.contributor.authorKAYA, ÖZLEM TUĞÇE
dc.contributor.authorÖZBEYLİ, DİLEK
dc.contributor.authorsAykac A., Mirzaei O., KAYA Ö. T. , ÖZBEYLİ D., Suer K.
dc.date.accessioned2022-12-23T12:22:36Z
dc.date.available2022-12-23T12:22:36Z
dc.date.issued2022-01-01
dc.description.abstract© 2022 IEEE.In an experimental rat model of acute cerulein-induced pancreatitis, we aimed to investigate the ability of the deep neural network-based program to distinguish damaged cell structures in histological preparations derived from rat pancreatic tissues. After the pancreatic tissues of all rats underwent the paraffin procedure, 3-4 pm thick sections were taken from the paraffin blocks, stained with hematoxylin-eosin dye, evaluated with a light microscope and photographed using a light microscope. 89 mixed-size microscopic images are resized at 224*224 diameter. The datasets were divided into train, validation and test groups. The algorithm used in this study was based on the NAS-Net-Mobile and ResNet-101 models from MATLAB Transfer Learning. By increasing the number of samples in the method we use in histology, both the evaluation performance and time consumption are reduced with the Al we use. The accuracy rate we obtained with NAS-Net mobile was determined to be higher than ResNet-101.
dc.identifier.citationAykac A., Mirzaei O., KAYA Ö. T. , ÖZBEYLİ D., Suer K., \"Deep Learning-Based Histological Scoring of Cerulein-Induced Acute Pancreatitis Rat Model\", 2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022, Nicosia, Türkiye, 2 - 04 Ağustos 2022, ss.75-78
dc.identifier.doi10.1109/aie57029.2022.00022
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140919646&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/283928
dc.language.isoeng
dc.relation.ispartof2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectMühendislik ve Teknoloji
dc.subjectInformation Systems, Communication and Control Engineering
dc.subjectComputer Sciences
dc.subjectalgorithms
dc.subjectEngineering and Technology
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectTELEKOMÜNİKASYON
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectCOMPUTER SCIENCE
dc.subjectENGINEERING
dc.subjectCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
dc.subjectTELECOMMUNICATIONS
dc.subjectBilgisayar Ağları ve İletişim
dc.subjectFizik Bilimleri
dc.subjectYapay Zeka
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectComputer Networks and Communications
dc.subjectPhysical Sciences
dc.subjectArtificial Intelligence
dc.subjectComputer Science Applications
dc.subjectacute pancreatitis
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.titleDeep learning-based histological scoring of cerulein-induced acute pancreatitis rat model
dc.typeconferenceObject
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoveryd9b3e5d1-ec49-49ec-887a-14cd84eb73f6

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