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dc.contributor.authorIrfan, Muhammad
dc.contributor.authorShahrestani, Seyed
dc.contributor.authorElKhodr, Mahmoud
dc.date2023-07
dc.date.accessioned2023-08-28T12:26:24Z
dc.date.available2023-08-28T12:26:24Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/15135
dc.description.abstractDetecting Alzheimer’s disease (AD) in its early stages is essential for effective management, and screening for Mild Cognitive Impairment (MCI) is common practice. Among many deep learning techniques applied to assess brain structural changes, Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNN) have grabbed research attention because of their excellent efficiency in automated feature learning of a variety of multilayer perceptron. In this study, various CNNs are trained to predict AD on three different views of MRI images, including Sagittal, Transverse, and Coronal views. This research use T1-Weighted MRI data of 3 years composed of 2182 NIFTI files. Each NIFTI file presents a single patient's Sagittal, Transverse, and Coronal views. T1-Weighted MRI images from the ADNI database are first preprocessed to achieve better representation. After MRI preprocessing, large slice numbers require a substantial computational cost during CNN training. To reduce the slice numbers for each view, this research proposes an intelligent probabilistic approach to select slice numbers such that the total computational cost per MRI is minimized. With hyperparameter tuning, batch normalization, and intelligent slice selection and cropping, an accuracy of 90.05% achieve with the Transverse, 82.4% with Sagittal, and 78.5% with Coronal view, respectively. Moreover, the views are stacked together and an accuracy of 92.21% is achived for the combined views. In addition, results are compared with other studies to show the performance of the proposed approach for AD detection.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;In Press
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/3354es_ES
dc.rightsopenAccesses_ES
dc.subjectAlzheimer's diseasees_ES
dc.subjectclassificationes_ES
dc.subjectcognitive computinges_ES
dc.subjectConvolutional Neural Network (CNN)es_ES
dc.subjectdeep learninges_ES
dc.subjectelectromagnetic optimizationes_ES
dc.subjectIJIMAIes_ES
dc.titleThe Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Dataes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2023.07.009


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