Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.
An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD ear...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 1; pp. 87 - 111 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135233776&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135233776 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Feb2019 vid: 13 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135233776 135233776 NLM29460167 10.1007/s11682-018-9831-2 NLM29460167 135233776 ppf: 87 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network. aug: au: Duraisamy, Baskar Shanmugam, Jayanthi Venkatraman Annamalai, Jayanthi affil: Hindusthan College of Engineering and Technology, Coimbatore, India sug: subj: Brain Image Interpretation, Computer Assisted Methods Alzheimer's Disease Magnetic Resonance Imaging Methods Aged, 80 and Over Middle Age Aged Sensitivity and Specificity Neural Networks (Computer) Male Information Science Methods Female Clinical Assessment Tools Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years Male Female ab: An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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