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fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging...

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Autores principales: Chelladurai, Aarthi, Narayan, Dayanand Lal, Divakarachari, Parameshachari Bidare, Loganathan, Umasankar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296435/
https://www.ncbi.nlm.nih.gov/pubmed/37371371
http://dx.doi.org/10.3390/brainsci13060893
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author Chelladurai, Aarthi
Narayan, Dayanand Lal
Divakarachari, Parameshachari Bidare
Loganathan, Umasankar
author_facet Chelladurai, Aarthi
Narayan, Dayanand Lal
Divakarachari, Parameshachari Bidare
Loganathan, Umasankar
author_sort Chelladurai, Aarthi
collection PubMed
description In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.
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spelling pubmed-102964352023-06-28 fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network Chelladurai, Aarthi Narayan, Dayanand Lal Divakarachari, Parameshachari Bidare Loganathan, Umasankar Brain Sci Article In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models. MDPI 2023-05-31 /pmc/articles/PMC10296435/ /pubmed/37371371 http://dx.doi.org/10.3390/brainsci13060893 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chelladurai, Aarthi
Narayan, Dayanand Lal
Divakarachari, Parameshachari Bidare
Loganathan, Umasankar
fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title_full fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title_fullStr fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title_full_unstemmed fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title_short fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network
title_sort fmri-based alzheimer’s disease detection using the sas method with multi-layer perceptron network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296435/
https://www.ncbi.nlm.nih.gov/pubmed/37371371
http://dx.doi.org/10.3390/brainsci13060893
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