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dc.contributor.authorDiker, Aykut
dc.contributor.authorElen, Abdullah
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-14T10:57:19Z
dc.date.available2023-03-14T10:57:19Z
dc.date.issued2023-01-01
dc.identifier.citationAykut Diker, Abdullah Elen, Abdulhamit Subasi, Chapter 7 - Brain stroke detection from computed tomography images using deep learning algorithms, Editor(s): Abdulhamit Subasi, In Artificial Intelligence Applications in Healthcare&Medicine, Applications of Artificial Intelligence in Medical Imaging, Academic Press, 2023, Pages 207-222, ISBN 9780443184505, https://doi.org/10.1016/B978-0-443-18450-5.00013-X.en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-443-18450-5.00013-Xen_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/660
dc.description.abstractStroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.en_US
dc.publisherAcademic Pressen_US
dc.subjectStroke Classificationen_US
dc.subjectResidual Convolutional Neural Networken_US
dc.subjectVGG-19en_US
dc.subjectAlexNeten_US
dc.subjectGoogleNeten_US
dc.titleBrain stroke detection from computed tomography images using deep learning algorithmsen_US
dc.source.booktitleApplications of Artificial Intelligence in Medical Imaging.en_US
dc.source.volumeArtificial Intelligence Applications in Healthcare&Medicine;
dc.source.pages207-222en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorDiker, Aykut


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