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dc.contributor.authorOzaltin, Oznur
dc.contributor.authorCoskun, Orhan
dc.contributor.authorYeniay, Ozgur
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-14T12:05:50Z
dc.date.available2023-03-14T12:05:50Z
dc.date.issued2022-12-01
dc.identifier.citationOzaltin, O.; Coskun, O.; Yeniay, O.; Subasi, A. A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. Bioengineering 2022, 9, 783. https://doi.org/10.3390/bioengineering9120783en_US
dc.identifier.doihttps://doi.org/10.3390/bioengineering9120783en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/668
dc.description.abstractA brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.en_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.subjectBrain Strokeen_US
dc.subjectClassificationen_US
dc.subjectConvolutional Neural Networks (CNNs)en_US
dc.subjectComputed Tomographyen_US
dc.subjectFeature Extractionen_US
dc.subjectmRMRen_US
dc.subjectOzNeten_US
dc.titleA Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNeten_US
dc.source.journalBioengineeringen_US
dc.source.volume9en_US
dc.source.issue12en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorOzaltin, Oznur


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