A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

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dc.contributor.authorArya, Akhilesh Deep
dc.contributor.authorVerma, Sourabh Singh
dc.contributor.authorChakarabarti, Prasun
dc.contributor.authorChakrabarti, Tulika
dc.contributor.authorElngar, Ahmed A.
dc.contributor.authorKamali, Ali-Mohammad
dc.contributor.authorNami, Mohammad
dc.date.accessioned2023-09-14T05:48:45Z
dc.date.available2023-09-14T05:48:45Z
dc.date.issued2023-12
dc.description.abstractAlzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers. © 2023, The Author(s).
dc.identifier.citationArya, A. D., Verma, S. S., Chakarabarti, P., Chakrabarti, T., Elngar, A. A., Kamali, A. M., & Nami, M. (2023). A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease. Brain Informatics, 10(1), 1-15. https://doi.org/10.1186/s40708-023-00195-7
dc.identifier.issn21984018
dc.identifier.urihttps://doi.org/10.1186/s40708-023-00195-7
dc.identifier.urihttps://hdl.handle.net/20.500.12519/828
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseriesBrain Informatics; Volume 10, Issue 1
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rights.holderCopyright : © 2023, The Author(s).
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlzheimer’s disease
dc.subjectDeep learning
dc.subjectDementia
dc.subjectMachine learning
dc.subjectMCI
dc.subjectNeurodegenerative
dc.titleA systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease
dc.typeArticle
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