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dc.contributor.authorSaratxaga, Cristina L.
dc.contributor.authorMoya, Iratxe
dc.contributor.authorPicón, Artzai
dc.contributor.authorAcosta, Marina
dc.contributor.authorMoreno-Fernandez-de-Leceta, Aitor
dc.contributor.authorGarrote, Estibaliz
dc.contributor.authorBereciartua-Perez, Arantza
dc.date.accessioned2021-09-17T06:54:42Z
dc.date.available2021-09-17T06:54:42Z
dc.date.issued2021-09-09
dc.identifier.citationSaratxaga, Cristina L., Iratxe Moya, Artzai Picón, Marina Acosta, Aitor Moreno-Fernandez-de-Leceta, Estibaliz Garrote, and Arantza Bereciartua-Perez. “MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction.” Journal of Personalized Medicine 11, no. 9 (September 9, 2021): 902. doi:10.3390/jpm11090902.en
dc.identifier.urihttp://hdl.handle.net/11556/1197
dc.description.abstractBackground: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Al though tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.en
dc.description.sponsorshipThis work was partially supported by the SUPREME project. This project has received funding from the Basque Government’s Industry Department HAZITEK program under agreement ZE-2019/00022. This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK program’s project ONKOTOOLS under agreement KK-2020/00069en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleMRI Deep Learning-Based Solution for Alzheimer’s Disease Predictionen
dc.typearticleen
dc.identifier.doi10.3390/jpm11090902en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsDeep learningen
dc.subject.keywordsClassificationen
dc.subject.keywordsAlzheimer’sen
dc.subject.keywordsAlzheimeren
dc.subject.keywordsMRIen
dc.subject.keywordsOASISen
dc.identifier.essn2075-4426en
dc.issue.number9en
dc.journal.titleJournal of Personalized Medicineen
dc.page.initial902en
dc.volume.number11en


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