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Alzheimer's disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier.

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Title: Alzheimer's disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier.
Authors: Goel, Tripti1 (AUTHOR) triptigoel@ece.nits.ac.in, Verma, Shradha1 (AUTHOR) shradha_rs@ece.nits.ac.in, Tanveer, M.2 (AUTHOR) mtanveer@iiti.ac.in, Suganthan, P.N.3 (AUTHOR) p.n.suganthan@qu.edu.qa
Source: Information Fusion. Jun2025, Vol. 118, pN.PAG-N.PAG. 1p.
Subjects: Alzheimer's disease, Magnetic resonance imaging, Temporal lobe, Diagnosis, Evolutionary algorithms
Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that involves gradual memory loss and eventually leads to severe cognitive decline at the final stage. Advanced neuroimaging modalities, including magnetic resonance imaging (MRI), prove advantageous in diagnosing the severity of the progression of AD. T1-W structural MRI and susceptibility-weighted imaging (SWI) are two of the most popular MRI sequences for medical diagnosis. The formation of tangles in the hippocampus is a major cause of AD development. Significant hippocampal loss and temporal lobe atrophy characterize the progression to AD, which can be visualized using T1-W structural MRI. Recent research has shown that persons with AD have higher levels of iron in their basal ganglia, which causes non-local changes in phase images due to variances in tissue susceptibility. SWI images use phase information to detect the magnetic disturbance and to visualize the iron lesions better. In this paper, we propose the fusion of MRI and SWI images to integrate the structural atrophies and iron lesions accumulation in AD patients. Features from the fused images will be retrieved by a pre-trained deep learning network and categorized using a random vector functional link network (RVFL). Furthermore, a self-adaptive differential evolutionary algorithm will be used to fine-tune the RVFL network's input weights and biases. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available OASIS dataset. The source code of the proposed network is available at/github.com/triptigoel/SaDE-RVFL-for-AD-Diagnosis. • Alzheimer's disease is a form of dementia that predominantly affects elderly people. • T1-w MRI images show significant hippocampal loss and temporal lobe atrophy. • SWI images use phase information to detect the iron accumulation. • Fusion of MRI and SWI images is proposed to detect atrophies and iron accumulation. • Self-adaptive evolutionary algorithm is used to tune the RVFL network's parameters. [ABSTRACT FROM AUTHOR]
Copyright of Information Fusion is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 183382017
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Alzheimer's disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier.
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  Data: <searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Temporal+lobe%22">Temporal lobe</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink>
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  Data: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that involves gradual memory loss and eventually leads to severe cognitive decline at the final stage. Advanced neuroimaging modalities, including magnetic resonance imaging (MRI), prove advantageous in diagnosing the severity of the progression of AD. T1-W structural MRI and susceptibility-weighted imaging (SWI) are two of the most popular MRI sequences for medical diagnosis. The formation of tangles in the hippocampus is a major cause of AD development. Significant hippocampal loss and temporal lobe atrophy characterize the progression to AD, which can be visualized using T1-W structural MRI. Recent research has shown that persons with AD have higher levels of iron in their basal ganglia, which causes non-local changes in phase images due to variances in tissue susceptibility. SWI images use phase information to detect the magnetic disturbance and to visualize the iron lesions better. In this paper, we propose the fusion of MRI and SWI images to integrate the structural atrophies and iron lesions accumulation in AD patients. Features from the fused images will be retrieved by a pre-trained deep learning network and categorized using a random vector functional link network (RVFL). Furthermore, a self-adaptive differential evolutionary algorithm will be used to fine-tune the RVFL network's input weights and biases. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available OASIS dataset. The source code of the proposed network is available at/github.com/triptigoel/SaDE-RVFL-for-AD-Diagnosis. • Alzheimer's disease is a form of dementia that predominantly affects elderly people. • T1-w MRI images show significant hippocampal loss and temporal lobe atrophy. • SWI images use phase information to detect the iron accumulation. • Fusion of MRI and SWI images is proposed to detect atrophies and iron accumulation. • Self-adaptive evolutionary algorithm is used to tune the RVFL network's parameters. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Fusion is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.inffus.2024.102917
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Alzheimer's disease
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Temporal lobe
        Type: general
      – SubjectFull: Diagnosis
        Type: general
      – SubjectFull: Evolutionary algorithms
        Type: general
    Titles:
      – TitleFull: Alzheimer's disease diagnosis from MRI and SWI fused image using self adaptive differential evolutionary RVFL classifier.
        Type: main
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            NameFull: Goel, Tripti
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            NameFull: Verma, Shradha
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            NameFull: Tanveer, M.
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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              Value: 118
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            – TitleFull: Information Fusion
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