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dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorKapadnis, Manav Nitin
dc.contributor.authorBulbul, Ayse Kosal
dc.date.accessioned2023-03-13T09:12:53Z
dc.date.available2023-03-13T09:12:53Z
dc.date.issuedJanuary 2022
dc.identifier.citationbdulhamit Subasi, Manav Nitin Kapadnis, Ayse Kosal Bulbul, 4 - Alzheimer’s disease detection using artificial intelligence, Editor(s): Anitha S. Pillai, Bindu Menon, Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, Academic Press, 2022, Pages 53-74, ISBN 9780323900379, https://doi.org/10.1016/B978-0-323-90037-9.00011-4. (https://www.sciencedirect.com/science/article/pii/B9780323900379000114)en_US
dc.identifier.isbn9780323900379en_US
dc.identifier.doihttps://doi.org/10.1016/B978-0-323-90037-9.00011-4en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/616
dc.description.abstractBiomedical data relevant to several diseases are generally employed to diagnose precise physiological or pathological conditions. The objective of biomedical image analysis is exact modeling by using Artificial Intelligence (AI) algorithms to diagnose different diseases. Alzheimer’s disease (AD) is one of the most widespread dementia forms influencing the elderly people. On-time diagnosis of Alzheimer’s disease is crucial to discover innovative methods for AD treatment. AI is an efficient approach for AD detection since it can be utilized as a Computer-aided decision support systems approach in medical procedures and play a critical role to detect changes in the brain images to identify AD. This chapter presents the recent studies and advances in AI used for medical image analysis and image processing in AD detection. The main focus is to have a consistent but easy and quick model for automated AD detection relied on the application of AI methods. Hence, the focus will be on AI techniques for AD detection from brain images. Moreover, some of the AI techniques, which were utilized for AD detection is overviewed. Then a simple AD detection approach using deep learning models will be presented. The results show that CNN achieved a testing accuracy of 95.70% accuracy and a validation accuracy of 99.71% for the diagnosis of AD from brain MRI scans. The chapter will be completed with a review of the current state-of-the-art, a discussion of new trends and open challenges for potential investigation.en_US
dc.publisherAcademic Pressen_US
dc.subjectAlzheimer’s Disease Detectionen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectTransfer Learningen_US
dc.titleAlzheimer’s disease detection using artificial intelligenceen_US
dc.source.booktitleAugmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligenceen_US
dc.source.pages53-74en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorSubasi, Abdulhamit


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