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dc.contributor.authorOzaltin, Oznur
dc.contributor.authorCoskun, Orhan
dc.contributor.authorYeniay, Ozgur
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-14T10:54:25Z
dc.date.available2023-03-14T10:54:25Z
dc.date.issued2023-01-12
dc.identifier.doihttps://doi.org/10.1002/ima.22806en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/659
dc.description.abstractClassification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.en_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.subjectNeighborhood Component Analysis (NCA)en_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectConvolutional Neural Networks (CNN)en_US
dc.subjectK-Nearest Neighbor (K-NN)en_US
dc.subjectLinear Discriminant Analysis (LDA)en_US
dc.subjectSupport Vector Machines (SVM)en_US
dc.subjectNaïve Bayesen_US
dc.titleClassification of brain hemorrhage computed tomography images using OzNet hybrid algorithmen_US
dc.source.journalInternational Journal of Imaging Systems and Technologyen_US
dc.source.volume33en_US
dc.source.issue1en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorOznur, Ozaltin


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