Exemplar deep and hand-modeled features based automated and accurate cerebral hemorrhage classification method

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dc.authoriddurak, mehmet akif akif/0000-0003-0827-2708
dc.authoridDOGAN, Sengul/0000-0001-9677-5684
dc.authoridAKBAL, Erhan/0000-0002-5257-7560
dc.authoridDin, Mustafa sait/0000-0003-1343-3318
dc.authorwosidYıldırım, İsmail Okan/AFR-8243-2022
dc.authorwosidAKBAL, Erhan/W-4823-2018
dc.authorwosidTUNCER, Turker/W-4846-2018
dc.authorwosiddurak, mehmet akif akif/ABI-1169-2020
dc.authorwosidDOGAN, Sengul/W-4854-2018
dc.contributor.authorDin, M. Sait
dc.contributor.authorGurbuz, Sukru
dc.contributor.authorAkbal, Erhan
dc.contributor.authorDogan, Sengul
dc.contributor.authorDurak, M. Akif
dc.contributor.authorYildirim, I. Okan
dc.contributor.authorTuncer, Turker
dc.date.accessioned2024-08-04T20:51:59Z
dc.date.available2024-08-04T20:51:59Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: : Cerebral hemorrhage (CH) is a commonly seen disease, and an accurate diagnosis of the type of CH is a very crucial step in treatment. Therefore, CH requires a prompt and accurate diagnosis. To simplify this process, an accurate CH classification model is presented using a machine learning technique. Material and method: : A computed tomography (CT) image dataset was collected retrospectively in this research. This dataset contains 9818 images with five categories. An exemplar fused feature generator is presented to classify these features. This generator uses pre-trained AlexNet, local binary pattern (LBP), and local phase quantization (LPQ). The neighborhood component analysis (NCA) method selects the top features, and the chosen feature vector is classified on the support vector machine. Results: : Six validation methods are utilized to calculate the performance of the presented exemplar fused features and NCA-based CH classification model. This model attained 97.47%, 96.05%, 95.21%, 93.62%, 91.28% and 96.34% accuracies using five hold-out validations and ten-fold cross-validation respectively. Conclusions: : The calculated results clearly demonstrate the success and robustness of the introduced exemplar fused feature generation and NCA-based model. Furthermore, this model can be used in emergency services to overcome a prompt diagnosis of CH.en_US
dc.identifier.doi10.1016/j.medengphy.2022.103819
dc.identifier.issn1350-4533
dc.identifier.issn1873-4030
dc.identifier.pmid35781383en_US
dc.identifier.scopus2-s2.0-85131086329en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1016/j.medengphy.2022.103819
dc.identifier.urihttps://hdl.handle.net/11616/100681
dc.identifier.volume105en_US
dc.identifier.wosWOS:000807472700002en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMedical Engineering & Physicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExemplar fused feature generationen_US
dc.subjectCerebral hemorrhage identificationen_US
dc.subjectTransfer learningen_US
dc.subjectHand-modeled feature extractionen_US
dc.subjectSmart health assistanten_US
dc.titleExemplar deep and hand-modeled features based automated and accurate cerebral hemorrhage classification methoden_US
dc.typeArticleen_US

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