NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging.
Alzheimer's disease is a progressive neurodegenerative disorder that is challenging to diagnose at an early stage. Affecting over 55 million people worldwide, its prevalence is expected to rise sharply by 2030. The use of artificial intelligence (AI) techniques has become increasingly important to i...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 4; pp. 2916 - 2935 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2026
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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=196241787&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196241787 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2026 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196241787 190288127 196241787 196241787 10.1007/s10278-025-01706-0 196241787 ppf: 2916 ppct: 19 formats: tig: atl: NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging. aug: au: Özdemir, Esra Yüzgeç Koç, Canan Özyurt, Fatih affil: https://ror.org/05teb7b63 Software Engineering, Engineering Faculty, Firat University, Elazığ, Turkey sug: subj: Alzheimer's Disease Diagnosis Magnetic Resonance Imaging Diagnosis, Computer Assisted Artificial Intelligence Early Diagnosis Sensitivity and Specificity Image Interpretation, Computer Assisted Classification Algorithms Ensemble Learning Algorithms Motivation Imaging, Three-Dimensional Models, Statistical Quality Improvement Health Screening ab: Alzheimer's disease is a progressive neurodegenerative disorder that is challenging to diagnose at an early stage. Affecting over 55 million people worldwide, its prevalence is expected to rise sharply by 2030. The use of artificial intelligence (AI) techniques has become increasingly important to improve the speed and accuracy of diagnosis. In this study, we propose the NCA-Enhanced Voting Algorithm for Alzheimer's Classification (NCA-EVA) to support computer-aided diagnosis. A total of 66 models were trained for four-class data and six models for two-class data. The proposed method successfully classified all four stages of Alzheimer's disease, achieving 98.97% accuracy in four-class classification and 99.87% accuracy in binary classification. Moreover, with a processing time of just 1.26 s, NCA-EVA is approximately 1200 times faster than a comparable study using NCA-based feature selection. These findings demonstrate that Alzheimer's diagnosis can be performed both quickly and with high accuracy, and the proposed approach has potential applications in other healthcare data analysis tasks. pubtype: Academic Journal doctype: diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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