Brain Stroke Detection from CT Images using Transfer Learning Method

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dc.contributor.authorÇınar, Necip
dc.contributor.authorKaya, Buket
dc.contributor.authorKaya, Mehmet
dc.contributor.orcid0000-0002-5106-6240
dc.date.accessioned2024-04-24T17:56:24Z
dc.date.available2024-04-24T17:56:24Z
dc.date.issued2023
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description13th International Conference on Advanced Computer Information Technologies, ACIT 2023 -- 21 September 2023 through 23 September 2023 -- -- 193540en_US
dc.description.abstractRecently, with the production of high-capacity computers, artificial intelligence methods have been used in many areas. In particular, in the field of health, artificial intelligence methods are used to detect diseases and determine the treatments to be applied. Deep learning methods, a sub-branch of artificial intelligence, show a high success in diagnosing many diseases thanks to its deep CNN networks. In this study, firstly, it was tried to determine which deep learning methods are more successful for the detection of brain stroke from computerized tomography images. This study is of great importance in terms of determining which deep learning architectures we will focus on in our future studies on brain stroke. In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. As a result, the EfficientNet-B0 architecture showed the highest performance with 97.93% accuracy. ResNetl01, VGG19, MobileNet-V2 and GoogleNet models showed 94.32%, 97.28%, 92.30% and 91.61% accuracy rates, respectively. With this study, it was concluded that the EfficientNet architecture is more suitable for the detection of brain stroke disease from CT images.en_US
dc.identifier.citationÇınar, N., Kaya, B. ve Kaya, M. (2023). Brain stroke detection from CT images using transfer learning method. Proceedings - International Conference on Advanced Computer Information Technologies, ACIT, 595-599.
dc.identifier.doi10.1109/ACIT58437.2023.10275682
dc.identifier.endpage599en_US
dc.identifier.issn2770-5218
dc.identifier.scopus2-s2.0-85175569264
dc.identifier.scopusqualityN/A
dc.identifier.startpage595en_US
dc.identifier.urihttps://doi.org/10.1109/ACIT58437.2023.10275682
dc.identifier.urihttps://hdl.handle.net/11468/23494
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.relation.ispartofProceedings - International Conference on Advanced Computer Information Technologies, ACIT
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain Strokeen_US
dc.subjectCnnen_US
dc.subjectDeep Learningen_US
dc.subjectDeep Neural Networksen_US
dc.subjectTransfer Learningen_US
dc.titleBrain Stroke Detection from CT Images using Transfer Learning Methoden_US
dc.titleBrain Stroke Detection from CT Images using Transfer Learning Method
dc.typeConference Objecten_US

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