Deep learning based approach with efficientNet and SE block attention mechanism for multiclass alzheimer's disease detection

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dc.authorid0000-0001-9219-2262en_US
dc.authorid0000-0003-2995-8282en_US
dc.contributor.authorUçan, Sevilay
dc.contributor.authorUçan, Murat
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2024-09-12T06:11:27Z
dc.date.available2024-09-12T06:11:27Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractAlzheimer's disease can be detected beforehand using brain MRI images. However, this situation requires a specialist in the field and is a very open area to human error. In addition, it takes a long time for specialist doctors to analyze images and make decisions by diagnosing diseases. In this study, it is aimed to detect Alzheimer's disease quickly and with high accuracy by using deep learning architectures. In addition, with the use of the proposed study in hospitals, it can play an important role in reducing false treatments by supporting doctors in disease detection. In the study, open source licensed brain MR images dataset obtained from Kaggle was used. The dataset is divided into 3 subgroups as training, validation and testing. Within the scope of the work, where EfficientNet-B0 architecture and SE block attention mechanisms were used, a model specific to the data set was developed and used. The results of the studies were compared with other studies using the same data set and detailed results were given in the study. Studies have shown that Brain MRI images can be successfully classified with an accuracy of 0.9903. The findings suggested that the architecture created might help reduce human error in diagnosing Alzheimer's disease.en_US
dc.identifier.citationUçan, S., Uçan, M. ve Kaya, M. (2023). Deep learning based approach with efficientNet and SE block attention mechanism for multiclass alzheimer's disease detection. 2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023 içinde, 285-289.en_US
dc.identifier.endpage289en_US
dc.identifier.isbn979-835036978-6
dc.identifier.scopus2-s2.0-85202449059
dc.identifier.scopusqualityN/A
dc.identifier.startpage285en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/10629380
dc.identifier.urihttps://hdl.handle.net/11468/28838
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Sevilay
dc.institutionauthorUçan, Murat
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023
dc.relation.isversionof10.1109/ICDABI60145.2023.10629380en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlzheimeren_US
dc.subjectAttentionen_US
dc.subjectClassificationen_US
dc.subjectCNNen_US
dc.subjectEfficientNeten_US
dc.subjectMRIen_US
dc.titleDeep learning based approach with efficientNet and SE block attention mechanism for multiclass alzheimer's disease detectionen_US
dc.titleDeep learning based approach with efficientNet and SE block attention mechanism for multiclass alzheimer's disease detection
dc.typeConference Objecten_US

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